{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import joblib\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>A1</th>\n",
       "      <th>Ea1</th>\n",
       "      <th>A2</th>\n",
       "      <th>Ea2</th>\n",
       "      <th>A3</th>\n",
       "      <th>Ea3</th>\n",
       "      <th>A4</th>\n",
       "      <th>Ea4</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "      <th>Fast_rxn1</th>\n",
       "      <th>Medium_rxn1</th>\n",
       "      <th>Slow_rxn1</th>\n",
       "      <th>Fast_rxn2</th>\n",
       "      <th>Medium_rxn2</th>\n",
       "      <th>Slow_rxn2</th>\n",
       "      <th>Reaction_order</th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>1933581</td>\n",
       "      <td>273</td>\n",
       "      <td>261.472219</td>\n",
       "      <td>194</td>\n",
       "      <td>0.044413</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.236088e-02</td>\n",
       "      <td>3.977472e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>1933582</td>\n",
       "      <td>273</td>\n",
       "      <td>596.074861</td>\n",
       "      <td>62</td>\n",
       "      <td>8.957436</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.985276e-06</td>\n",
       "      <td>7.871450e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>1933583</td>\n",
       "      <td>273</td>\n",
       "      <td>423.789150</td>\n",
       "      <td>55</td>\n",
       "      <td>904.812139</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.042629e-07</td>\n",
       "      <td>7.955867e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>1933584</td>\n",
       "      <td>273</td>\n",
       "      <td>0.014179</td>\n",
       "      <td>123</td>\n",
       "      <td>0.061371</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.618686e-03</td>\n",
       "      <td>8.963359e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>1933585</td>\n",
       "      <td>273</td>\n",
       "      <td>0.045442</td>\n",
       "      <td>179</td>\n",
       "      <td>1.544173</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.689157e-03</td>\n",
       "      <td>2.451002e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 258 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  Temperature          A1  Ea1          A2  Ea2  A3  Ea3  \\\n",
       "1933581     1933581          273  261.472219  194    0.044413  141   0    0   \n",
       "1933582     1933582          273  596.074861   62    8.957436    9   0    0   \n",
       "1933583     1933583          273  423.789150   55  904.812139  184   0    0   \n",
       "1933584     1933584          273    0.014179  123    0.061371   74   0    0   \n",
       "1933585     1933585          273    0.045442  179    1.544173  121   0    0   \n",
       "\n",
       "         A4  Ea4  ...  cINT1_10800s  cINT1_14400s  Fast_rxn1  Medium_rxn1  \\\n",
       "1933581   0    0  ...  4.236088e-02  3.977472e-02          1            0   \n",
       "1933582   0    0  ...  9.985276e-06  7.871450e-06          1            0   \n",
       "1933583   0    0  ...  1.042629e-07  7.955867e-08          1            0   \n",
       "1933584   0    0  ...  8.618686e-03  8.963359e-03          0            0   \n",
       "1933585   0    0  ...  2.689157e-03  2.451002e-03          0            0   \n",
       "\n",
       "         Slow_rxn1  Fast_rxn2  Medium_rxn2  Slow_rxn2  \\\n",
       "1933581          0          0            0          1   \n",
       "1933582          0          0            1          0   \n",
       "1933583          0          1            0          0   \n",
       "1933584          1          0            0          1   \n",
       "1933585          1          0            1          0   \n",
       "\n",
       "                                            Reaction_order  Mechanism  \n",
       "1933581  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933582  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933583  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933584  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933585  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "\n",
       "[5 rows x 258 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv('../two_reactions_022624.csv')\n",
    "df_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 258)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mechanism\n",
       "1933581  (93, 254)\n",
       "1933582  (93, 254)\n",
       "1933583  (93, 254)\n",
       "1933584  (93, 254)\n",
       "1933585  (93, 254)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_raw_df = df_raw.iloc[:, -1:]\n",
    "y_raw_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "unique_values = y_raw_df['Mechanism'].unique()\n",
    "print(len(unique_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 258)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_raw.copy()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "conc_list = [col for col in df.columns if col.endswith('s')]\n",
    "# conc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299999</td>\n",
       "      <td>0.299992</td>\n",
       "      <td>0.299837</td>\n",
       "      <td>0.299675</td>\n",
       "      <td>0.299514</td>\n",
       "      <td>0.299035</td>\n",
       "      <td>0.298564</td>\n",
       "      <td>0.298101</td>\n",
       "      <td>0.297645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006314</td>\n",
       "      <td>0.006981</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>0.007767</td>\n",
       "      <td>0.007898</td>\n",
       "      <td>0.007860</td>\n",
       "      <td>0.007791</td>\n",
       "      <td>0.007491</td>\n",
       "      <td>0.007172</td>\n",
       "      <td>0.006560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.299999</td>\n",
       "      <td>0.299978</td>\n",
       "      <td>0.299957</td>\n",
       "      <td>0.299935</td>\n",
       "      <td>0.299870</td>\n",
       "      <td>0.299805</td>\n",
       "      <td>0.299740</td>\n",
       "      <td>0.299675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.299996</td>\n",
       "      <td>0.299926</td>\n",
       "      <td>0.299853</td>\n",
       "      <td>0.299780</td>\n",
       "      <td>0.299561</td>\n",
       "      <td>0.299344</td>\n",
       "      <td>0.299129</td>\n",
       "      <td>0.298914</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299989</td>\n",
       "      <td>0.299887</td>\n",
       "      <td>0.297811</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>0.293877</td>\n",
       "      <td>0.288868</td>\n",
       "      <td>0.284657</td>\n",
       "      <td>0.281052</td>\n",
       "      <td>0.277897</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017600</td>\n",
       "      <td>0.016738</td>\n",
       "      <td>0.015892</td>\n",
       "      <td>0.014708</td>\n",
       "      <td>0.013685</td>\n",
       "      <td>0.012807</td>\n",
       "      <td>0.012051</td>\n",
       "      <td>0.010814</td>\n",
       "      <td>0.009851</td>\n",
       "      <td>0.008435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299941</td>\n",
       "      <td>0.299417</td>\n",
       "      <td>0.290150</td>\n",
       "      <td>0.282960</td>\n",
       "      <td>0.277360</td>\n",
       "      <td>0.265672</td>\n",
       "      <td>0.257948</td>\n",
       "      <td>0.252221</td>\n",
       "      <td>0.247715</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.003784</td>\n",
       "      <td>0.003389</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>0.002632</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.002182</td>\n",
       "      <td>0.001874</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>0.001348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "0     0.3  0.299999  0.299992  0.299837  0.299675  0.299514  0.299035   \n",
       "1     0.3  0.300000  0.299999  0.299978  0.299957  0.299935  0.299870   \n",
       "2     0.3  0.300000  0.299996  0.299926  0.299853  0.299780  0.299561   \n",
       "3     0.3  0.299989  0.299887  0.297811  0.295775  0.293877  0.288868   \n",
       "4     0.3  0.299941  0.299417  0.290150  0.282960  0.277360  0.265672   \n",
       "\n",
       "   cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  cINT1_3600s  \\\n",
       "0  0.298564  0.298101  0.297645  ...     0.006314     0.006981     0.007418   \n",
       "1  0.299805  0.299740  0.299675  ...     0.000503     0.000512     0.000509   \n",
       "2  0.299344  0.299129  0.298914  ...     0.000119     0.000117     0.000115   \n",
       "3  0.284657  0.281052  0.277897  ...     0.017600     0.016738     0.015892   \n",
       "4  0.257948  0.252221  0.247715  ...     0.004310     0.003784     0.003389   \n",
       "\n",
       "   cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  cINT1_9000s  \\\n",
       "0     0.007767     0.007898     0.007860     0.007791     0.007491   \n",
       "1     0.000498     0.000495     0.000490     0.000485     0.000474   \n",
       "2     0.000111     0.000108     0.000106     0.000103     0.000099   \n",
       "3     0.014708     0.013685     0.012807     0.012051     0.010814   \n",
       "4     0.002952     0.002632     0.002379     0.002182     0.001874   \n",
       "\n",
       "   cINT1_10800s  cINT1_14400s  \n",
       "0      0.007172      0.006560  \n",
       "1      0.000466      0.000450  \n",
       "2      0.000095      0.000088  \n",
       "3      0.009851      0.008435  \n",
       "4      0.001653      0.001348  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df_raw = df[conc_list]\n",
    "x_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a MinMaxScaler instance\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Transpose the DataFrame so that rows become columns for scaling\n",
    "x_df_transposed = x_df_raw.T\n",
    "\n",
    "# Scale the entire transposed DataFrame\n",
    "scaled_data = scaler.fit_transform(x_df_transposed)\n",
    "\n",
    "# Transpose the scaled data back to the original orientation\n",
    "x = pd.DataFrame(scaled_data.T, columns=x_df_raw.columns, index=x_df_raw.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333324</td>\n",
       "      <td>0.333152</td>\n",
       "      <td>0.332972</td>\n",
       "      <td>0.332793</td>\n",
       "      <td>0.332261</td>\n",
       "      <td>0.331738</td>\n",
       "      <td>0.331224</td>\n",
       "      <td>0.330717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>0.007757</td>\n",
       "      <td>0.008242</td>\n",
       "      <td>0.008630</td>\n",
       "      <td>0.008775</td>\n",
       "      <td>0.008734</td>\n",
       "      <td>0.008657</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.007969</td>\n",
       "      <td>0.007289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333309</td>\n",
       "      <td>0.333285</td>\n",
       "      <td>0.333261</td>\n",
       "      <td>0.333189</td>\n",
       "      <td>0.333117</td>\n",
       "      <td>0.333045</td>\n",
       "      <td>0.332973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000559</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333329</td>\n",
       "      <td>0.333252</td>\n",
       "      <td>0.333170</td>\n",
       "      <td>0.333089</td>\n",
       "      <td>0.332846</td>\n",
       "      <td>0.332605</td>\n",
       "      <td>0.332365</td>\n",
       "      <td>0.332127</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333321</td>\n",
       "      <td>0.333207</td>\n",
       "      <td>0.330901</td>\n",
       "      <td>0.328639</td>\n",
       "      <td>0.326530</td>\n",
       "      <td>0.320964</td>\n",
       "      <td>0.316285</td>\n",
       "      <td>0.312280</td>\n",
       "      <td>0.308774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019555</td>\n",
       "      <td>0.018597</td>\n",
       "      <td>0.017658</td>\n",
       "      <td>0.016342</td>\n",
       "      <td>0.015205</td>\n",
       "      <td>0.014230</td>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.012016</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>0.009372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333268</td>\n",
       "      <td>0.332686</td>\n",
       "      <td>0.322389</td>\n",
       "      <td>0.314400</td>\n",
       "      <td>0.308178</td>\n",
       "      <td>0.295191</td>\n",
       "      <td>0.286609</td>\n",
       "      <td>0.280245</td>\n",
       "      <td>0.275239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004789</td>\n",
       "      <td>0.004205</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>0.002424</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.001837</td>\n",
       "      <td>0.001498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "0  0.333333  0.333332  0.333324  0.333152  0.332972  0.332793  0.332261   \n",
       "1  0.333333  0.333333  0.333332  0.333309  0.333285  0.333261  0.333189   \n",
       "2  0.333333  0.333333  0.333329  0.333252  0.333170  0.333089  0.332846   \n",
       "3  0.333333  0.333321  0.333207  0.330901  0.328639  0.326530  0.320964   \n",
       "4  0.333333  0.333268  0.332686  0.322389  0.314400  0.308178  0.295191   \n",
       "\n",
       "   cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  cINT1_3600s  \\\n",
       "0  0.331738  0.331224  0.330717  ...     0.007016     0.007757     0.008242   \n",
       "1  0.333117  0.333045  0.332973  ...     0.000559     0.000569     0.000566   \n",
       "2  0.332605  0.332365  0.332127  ...     0.000133     0.000130     0.000127   \n",
       "3  0.316285  0.312280  0.308774  ...     0.019555     0.018597     0.017658   \n",
       "4  0.286609  0.280245  0.275239  ...     0.004789     0.004205     0.003766   \n",
       "\n",
       "   cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  cINT1_9000s  \\\n",
       "0     0.008630     0.008775     0.008734     0.008657     0.008323   \n",
       "1     0.000553     0.000549     0.000545     0.000539     0.000527   \n",
       "2     0.000124     0.000120     0.000117     0.000115     0.000110   \n",
       "3     0.016342     0.015205     0.014230     0.013390     0.012016   \n",
       "4     0.003280     0.002925     0.002643     0.002424     0.002082   \n",
       "\n",
       "   cINT1_10800s  cINT1_14400s  \n",
       "0      0.007969      0.007289  \n",
       "1      0.000517      0.000500  \n",
       "2      0.000105      0.000098  \n",
       "3      0.010945      0.009372  \n",
       "4      0.001837      0.001498  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mechanism\n",
       "1933581  (93, 254)\n",
       "1933582  (93, 254)\n",
       "1933583  (93, 254)\n",
       "1933584  (93, 254)\n",
       "1933585  (93, 254)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df = df.iloc[:, -1:]\n",
    "y_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Mechanism\n",
      "0                1\n",
      "1                1\n",
      "2                1\n",
      "3                1\n",
      "4                1\n",
      "...            ...\n",
      "1933581         62\n",
      "1933582         62\n",
      "1933583         62\n",
      "1933584         62\n",
      "1933585         62\n",
      "\n",
      "[1933586 rows x 1 columns]\n",
      "{'(105, 197)': 1, '(105, 228)': 2, '(105, 229)': 3, '(108, 138)': 4, '(108, 170)': 5, '(108, 192)': 6, '(108, 224)': 7, '(108, 246)': 8, '(108, 278)': 9, '(108, 300)': 10, '(108, 332)': 11, '(109, 138)': 12, '(109, 139)': 13, '(109, 170)': 14, '(109, 171)': 15, '(109, 192)': 16, '(109, 193)': 17, '(109, 224)': 18, '(109, 225)': 19, '(93, 255)': 20, '(93, 262)': 21, '(93, 263)': 22, '(93, 308)': 23, '(93, 309)': 24, '(93, 316)': 25, '(93, 317)': 26, '(97, 128)': 27, '(97, 166)': 28, '(97, 167)': 29, '(97, 181)': 30, '(97, 220)': 31, '(97, 221)': 32, '(72, 204)': 33, '(72, 205)': 34, '(72, 212)': 35, '(72, 213)': 36, '(73, 150)': 37, '(73, 151)': 38, '(73, 158)': 39, '(73, 159)': 40, '(73, 204)': 41, '(73, 205)': 42, '(73, 212)': 43, '(73, 213)': 44, '(76, 146)': 45, '(76, 147)': 46, '(76, 154)': 47, '(76, 155)': 48, '(76, 200)': 49, '(76, 201)': 50, '(76, 208)': 51, '(76, 209)': 52, '(76, 254)': 53, '(76, 255)': 54, '(76, 262)': 55, '(76, 263)': 56, '(76, 308)': 57, '(76, 309)': 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150)': 115, '(51, 151)': 116, '(51, 158)': 117, '(51, 159)': 118, '(52, 146)': 119, '(52, 147)': 120, '(52, 154)': 121, '(52, 155)': 122, '(52, 254)': 123, '(52, 255)': 124, '(52, 262)': 125, '(52, 263)': 126, '(53, 146)': 127, '(53, 147)': 128, '(53, 154)': 129, '(53, 155)': 130, '(53, 254)': 131, '(53, 255)': 132, '(53, 262)': 133, '(53, 263)': 134, '(55, 128)': 135, '(55, 166)': 136, '(55, 167)': 137, '(55, 338)': 138, '(55, 386)': 139, '(57, 126)': 140, '(57, 162)': 141, '(57, 163)': 142, '(57, 232)': 143, '(57, 270)': 144, '(57, 271)': 145, '(57, 334)': 146, '(57, 382)': 147, '(57, 466)': 148, '(57, 514)': 149, '(58, 142)': 150, '(58, 174)': 151, '(59, 142)': 152, '(59, 143)': 153, '(59, 174)': 154, '(59, 175)': 155, '(60, 138)': 156, '(60, 170)': 157, '(60, 246)': 158, '(60, 278)': 159, '(61, 138)': 160, '(61, 139)': 161, '(61, 170)': 162, '(61, 171)': 163, '(61, 246)': 164, '(77, 316)': 165, '(77, 317)': 166, '(88, 150)': 167, '(88, 151)': 168, '(88, 158)': 169, '(88, 159)': 170, '(88, 204)': 171, '(88, 205)': 172, '(88, 212)': 173, '(88, 213)': 174, '(89, 150)': 175, '(89, 151)': 176, '(89, 158)': 177, '(89, 159)': 178, '(89, 204)': 179, '(89, 205)': 180, '(89, 212)': 181, '(89, 213)': 182, '(92, 146)': 183, '(92, 147)': 184, '(92, 154)': 185, '(92, 155)': 186, '(92, 200)': 187, '(72, 159)': 188, '(33, 139)': 189, '(33, 170)': 190, '(33, 171)': 191, '(33, 192)': 192, '(33, 193)': 193, '(33, 224)': 194, '(33, 225)': 195, '(37, 126)': 196, '(37, 162)': 197, '(37, 163)': 198, '(37, 179)': 199, '(37, 216)': 200, '(37, 217)': 201, '(37, 334)': 202, '(37, 382)': 203, '(37, 400)': 204, '(37, 448)': 205, '(40, 138)': 206, '(40, 170)': 207, '(40, 192)': 208, '(40, 224)': 209, '(41, 138)': 210, '(41, 139)': 211, '(41, 170)': 212, '(41, 171)': 213, '(41, 192)': 214, '(41, 193)': 215, '(41, 224)': 216, '(41, 225)': 217, '(42, 150)': 218, '(42, 151)': 219, '(42, 158)': 220, '(42, 159)': 221, '(43, 150)': 222, '(43, 151)': 223, '(43, 158)': 224, '(43, 159)': 225, '(44, 146)': 226, '(44, 147)': 227, '(44, 154)': 228, '(44, 155)': 229, '(44, 254)': 230, '(44, 255)': 231, '(44, 262)': 232, '(44, 263)': 233, '(45, 146)': 234, '(117, 179)': 235, '(117, 216)': 236, '(0, 146)': 237, '(0, 147)': 238, '(0, 154)': 239, '(0, 155)': 240, '(1, 146)': 241, '(1, 147)': 242, '(1, 154)': 243, '(1, 155)': 244, '(4, 146)': 245, '(4, 147)': 246, '(4, 154)': 247, '(4, 155)': 248, '(5, 146)': 249, '(5, 147)': 250, '(5, 154)': 251, '(5, 155)': 252, '(7, 126)': 253, '(7, 162)': 254, '(7, 163)': 255, '(7, 334)': 256, '(7, 382)': 257, '(8, 138)': 258, '(8, 170)': 259, '(9, 138)': 260, '(9, 139)': 261, '(9, 170)': 262, '(9, 171)': 263, '(11, 126)': 264, '(11, 162)': 265, '(11, 163)': 266, '(11, 334)': 267, '(11, 382)': 268, '(12, 138)': 269, '(12, 170)': 270, '(13, 138)': 271, '(13, 139)': 272, '(13, 170)': 273, '(13, 171)': 274, '(16, 146)': 275, '(16, 147)': 276, '(16, 154)': 277, '(16, 155)': 278, '(16, 200)': 279, '(16, 201)': 280, '(16, 208)': 281, '(16, 209)': 282, '(17, 146)': 283, '(17, 147)': 284, '(17, 154)': 285, '(17, 155)': 286, '(17, 200)': 287, '(17, 201)': 288, '(17, 208)': 289, '(17, 209)': 290, '(24, 146)': 291, '(24, 147)': 292, '(24, 154)': 293, '(24, 155)': 294, '(24, 200)': 295, '(24, 201)': 296, '(24, 208)': 297, '(24, 209)': 298, '(25, 146)': 299, '(25, 147)': 300, '(25, 154)': 301, '(25, 155)': 302, '(25, 200)': 303, '(25, 201)': 304, '(25, 208)': 305, '(25, 209)': 306, '(29, 126)': 307, '(29, 162)': 308, '(29, 163)': 309, '(29, 179)': 310, '(29, 216)': 311, '(29, 217)': 312, '(29, 334)': 313, '(29, 382)': 314, '(29, 400)': 315, '(29, 448)': 316, '(32, 138)': 317, '(32, 170)': 318, '(32, 192)': 319, '(32, 224)': 320, '(33, 138)': 321, '(109, 246)': 322, '(109, 247)': 323, '(109, 278)': 324, '(109, 279)': 325, '(109, 300)': 326, '(109, 301)': 327, '(117, 514)': 328, '(117, 532)': 329, '(117, 580)': 330, '(120, 142)': 331, '(120, 174)': 332, '(120, 196)': 333, '(120, 228)': 334, '(121, 142)': 335, '(121, 143)': 336, '(121, 174)': 337, '(121, 175)': 338, '(121, 196)': 339, '(121, 197)': 340, '(121, 228)': 341, '(121, 229)': 342, '(77, 146)': 343, '(77, 147)': 344, '(77, 154)': 345, '(77, 155)': 346, '(77, 200)': 347, '(77, 201)': 348, '(77, 208)': 349, '(77, 209)': 350, '(77, 254)': 351, '(77, 255)': 352, '(77, 262)': 353, '(77, 263)': 354, '(77, 308)': 355, '(77, 309)': 356, '(125, 333)': 357, '(117, 217)': 358, '(117, 232)': 359, '(117, 270)': 360, '(117, 271)': 361, '(117, 285)': 362, '(117, 324)': 363, '(117, 325)': 364, '(117, 334)': 365, '(117, 382)': 366, '(117, 400)': 367, '(117, 448)': 368, '(117, 466)': 369, '(97, 338)': 370, '(97, 386)': 371, '(97, 404)': 372, '(97, 452)': 373, '(101, 126)': 374, '(101, 162)': 375, '(101, 163)': 376, '(101, 179)': 377, '(101, 216)': 378, '(101, 217)': 379, '(101, 232)': 380, '(101, 270)': 381, '(101, 271)': 382, '(101, 285)': 383, '(101, 324)': 384, '(101, 325)': 385, '(101, 334)': 386, '(101, 382)': 387, '(101, 400)': 388, '(101, 448)': 389, '(101, 466)': 390, '(101, 514)': 391, '(101, 532)': 392, '(101, 580)': 393, '(104, 142)': 394, '(104, 174)': 395, '(104, 196)': 396, '(104, 228)': 397, '(105, 142)': 398, '(105, 143)': 399, '(105, 174)': 400, '(105, 175)': 401, '(105, 196)': 402, '(124, 138)': 403, '(124, 170)': 404, '(124, 192)': 405, '(124, 224)': 406, '(124, 246)': 407, '(124, 278)': 408, '(124, 300)': 409, '(124, 332)': 410, '(125, 138)': 411, '(125, 139)': 412, '(125, 170)': 413, '(125, 171)': 414, '(125, 192)': 415, '(125, 193)': 416, '(125, 224)': 417, '(61, 247)': 418, '(61, 278)': 419, '(61, 279)': 420, '(63, 128)': 421, '(63, 166)': 422, '(63, 167)': 423, '(63, 338)': 424, '(63, 386)': 425, '(65, 126)': 426, '(65, 162)': 427, '(65, 163)': 428, '(65, 232)': 429, '(65, 270)': 430, '(65, 271)': 431, '(65, 334)': 432, '(65, 382)': 433, '(65, 466)': 434, '(65, 514)': 435, '(66, 142)': 436, '(66, 174)': 437, '(67, 142)': 438, '(67, 143)': 439, '(67, 174)': 440, '(67, 175)': 441, '(68, 138)': 442, '(68, 170)': 443, '(68, 246)': 444, '(68, 278)': 445, '(69, 138)': 446, '(69, 139)': 447, '(69, 170)': 448, '(69, 171)': 449, '(69, 246)': 450, '(69, 247)': 451, '(69, 278)': 452, '(69, 279)': 453, '(72, 150)': 454, '(72, 151)': 455, '(72, 158)': 456}\n"
     ]
    }
   ],
   "source": [
    "# Create a copy of the original DataFrame\n",
    "y_df_factorized = y_df.copy()\n",
    "\n",
    "# Create a dictionary to store the mapping of original labels to sequential labels\n",
    "mechanism_dic = {}\n",
    "\n",
    "# Use factorize to get sequential labels\n",
    "y_df_factorized['Mechanism'], unique_labels = pd.factorize(y_df_factorized['Mechanism'])\n",
    "\n",
    "# Increment by 1 to match your requirement\n",
    "y_df_factorized['Mechanism'] += 1\n",
    "\n",
    "# Store the mapping in the dictionary\n",
    "mechanism_dic = dict(zip(unique_labels, range(1, len(unique_labels) + 1)))\n",
    "\n",
    "# Display the DataFrame with sequential labels\n",
    "print(y_df_factorized)\n",
    "\n",
    "# Display the dictionary\n",
    "print(mechanism_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "print(len(mechanism_dic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "unique_mechanism = df['Mechanism'].unique()\n",
    "print(len(unique_mechanism))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2. ML Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Split Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.327349</td>\n",
       "      <td>0.293590</td>\n",
       "      <td>0.202480</td>\n",
       "      <td>0.185327</td>\n",
       "      <td>0.176645</td>\n",
       "      <td>0.164088</td>\n",
       "      <td>0.158029</td>\n",
       "      <td>0.154277</td>\n",
       "      <td>0.151664</td>\n",
       "      <td>...</td>\n",
       "      <td>6.275848e-02</td>\n",
       "      <td>6.047909e-02</td>\n",
       "      <td>5.857770e-02</td>\n",
       "      <td>5.622427e-02</td>\n",
       "      <td>5.429247e-02</td>\n",
       "      <td>5.266523e-02</td>\n",
       "      <td>5.125899e-02</td>\n",
       "      <td>4.893444e-02</td>\n",
       "      <td>4.706765e-02</td>\n",
       "      <td>4.419414e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.320007</td>\n",
       "      <td>0.267789</td>\n",
       "      <td>0.181156</td>\n",
       "      <td>0.167704</td>\n",
       "      <td>0.161147</td>\n",
       "      <td>0.152048</td>\n",
       "      <td>0.147856</td>\n",
       "      <td>0.145341</td>\n",
       "      <td>0.143635</td>\n",
       "      <td>...</td>\n",
       "      <td>4.819785e-05</td>\n",
       "      <td>3.918124e-05</td>\n",
       "      <td>3.289082e-05</td>\n",
       "      <td>2.668646e-05</td>\n",
       "      <td>2.236475e-05</td>\n",
       "      <td>1.936135e-05</td>\n",
       "      <td>1.689938e-05</td>\n",
       "      <td>1.375380e-05</td>\n",
       "      <td>1.109475e-05</td>\n",
       "      <td>8.746055e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.323456</td>\n",
       "      <td>0.278272</td>\n",
       "      <td>0.188772</td>\n",
       "      <td>0.173904</td>\n",
       "      <td>0.166546</td>\n",
       "      <td>0.156165</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>0.148334</td>\n",
       "      <td>0.146305</td>\n",
       "      <td>...</td>\n",
       "      <td>4.775315e-07</td>\n",
       "      <td>3.885170e-07</td>\n",
       "      <td>3.280314e-07</td>\n",
       "      <td>2.669785e-07</td>\n",
       "      <td>2.243345e-07</td>\n",
       "      <td>1.943700e-07</td>\n",
       "      <td>1.704890e-07</td>\n",
       "      <td>1.384731e-07</td>\n",
       "      <td>1.158476e-07</td>\n",
       "      <td>8.839853e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333330</td>\n",
       "      <td>0.333262</td>\n",
       "      <td>0.333191</td>\n",
       "      <td>0.333120</td>\n",
       "      <td>0.332907</td>\n",
       "      <td>0.332695</td>\n",
       "      <td>0.332484</td>\n",
       "      <td>0.332274</td>\n",
       "      <td>...</td>\n",
       "      <td>3.818451e-03</td>\n",
       "      <td>4.606387e-03</td>\n",
       "      <td>5.326639e-03</td>\n",
       "      <td>6.277075e-03</td>\n",
       "      <td>7.080558e-03</td>\n",
       "      <td>7.749471e-03</td>\n",
       "      <td>8.297305e-03</td>\n",
       "      <td>9.092941e-03</td>\n",
       "      <td>9.576317e-03</td>\n",
       "      <td>9.959288e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333322</td>\n",
       "      <td>0.333111</td>\n",
       "      <td>0.332889</td>\n",
       "      <td>0.332668</td>\n",
       "      <td>0.332013</td>\n",
       "      <td>0.331366</td>\n",
       "      <td>0.330728</td>\n",
       "      <td>0.330098</td>\n",
       "      <td>...</td>\n",
       "      <td>4.149791e-03</td>\n",
       "      <td>4.047360e-03</td>\n",
       "      <td>3.932019e-03</td>\n",
       "      <td>3.763374e-03</td>\n",
       "      <td>3.611976e-03</td>\n",
       "      <td>3.478599e-03</td>\n",
       "      <td>3.359512e-03</td>\n",
       "      <td>3.156144e-03</td>\n",
       "      <td>2.987952e-03</td>\n",
       "      <td>2.723336e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "1933581  0.333333  0.327349  0.293590  0.202480  0.185327  0.176645  0.164088   \n",
       "1933582  0.333333  0.320007  0.267789  0.181156  0.167704  0.161147  0.152048   \n",
       "1933583  0.333333  0.323456  0.278272  0.188772  0.173904  0.166546  0.156165   \n",
       "1933584  0.333333  0.333333  0.333330  0.333262  0.333191  0.333120  0.332907   \n",
       "1933585  0.333333  0.333332  0.333322  0.333111  0.332889  0.332668  0.332013   \n",
       "\n",
       "         cSM_180s  cSM_240s  cSM_300s  ...   cINT1_2400s   cINT1_3000s  \\\n",
       "1933581  0.158029  0.154277  0.151664  ...  6.275848e-02  6.047909e-02   \n",
       "1933582  0.147856  0.145341  0.143635  ...  4.819785e-05  3.918124e-05   \n",
       "1933583  0.151293  0.148334  0.146305  ...  4.775315e-07  3.885170e-07   \n",
       "1933584  0.332695  0.332484  0.332274  ...  3.818451e-03  4.606387e-03   \n",
       "1933585  0.331366  0.330728  0.330098  ...  4.149791e-03  4.047360e-03   \n",
       "\n",
       "          cINT1_3600s   cINT1_4500s   cINT1_5400s   cINT1_6300s   cINT1_7200s  \\\n",
       "1933581  5.857770e-02  5.622427e-02  5.429247e-02  5.266523e-02  5.125899e-02   \n",
       "1933582  3.289082e-05  2.668646e-05  2.236475e-05  1.936135e-05  1.689938e-05   \n",
       "1933583  3.280314e-07  2.669785e-07  2.243345e-07  1.943700e-07  1.704890e-07   \n",
       "1933584  5.326639e-03  6.277075e-03  7.080558e-03  7.749471e-03  8.297305e-03   \n",
       "1933585  3.932019e-03  3.763374e-03  3.611976e-03  3.478599e-03  3.359512e-03   \n",
       "\n",
       "          cINT1_9000s  cINT1_10800s  cINT1_14400s  \n",
       "1933581  4.893444e-02  4.706765e-02  4.419414e-02  \n",
       "1933582  1.375380e-05  1.109475e-05  8.746055e-06  \n",
       "1933583  1.384731e-07  1.158476e-07  8.839853e-08  \n",
       "1933584  9.092941e-03  9.576317e-03  9.959288e-03  \n",
       "1933585  3.156144e-03  2.987952e-03  2.723336e-03  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mechanism_(0, 146)</th>\n",
       "      <th>Mechanism_(0, 147)</th>\n",
       "      <th>Mechanism_(0, 154)</th>\n",
       "      <th>Mechanism_(0, 155)</th>\n",
       "      <th>Mechanism_(1, 146)</th>\n",
       "      <th>Mechanism_(1, 147)</th>\n",
       "      <th>Mechanism_(1, 154)</th>\n",
       "      <th>Mechanism_(1, 155)</th>\n",
       "      <th>Mechanism_(101, 126)</th>\n",
       "      <th>Mechanism_(101, 162)</th>\n",
       "      <th>...</th>\n",
       "      <th>Mechanism_(97, 128)</th>\n",
       "      <th>Mechanism_(97, 166)</th>\n",
       "      <th>Mechanism_(97, 167)</th>\n",
       "      <th>Mechanism_(97, 181)</th>\n",
       "      <th>Mechanism_(97, 220)</th>\n",
       "      <th>Mechanism_(97, 221)</th>\n",
       "      <th>Mechanism_(97, 338)</th>\n",
       "      <th>Mechanism_(97, 386)</th>\n",
       "      <th>Mechanism_(97, 404)</th>\n",
       "      <th>Mechanism_(97, 452)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 456 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mechanism_(0, 146)  Mechanism_(0, 147)  Mechanism_(0, 154)  \\\n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(0, 155)  Mechanism_(1, 146)  Mechanism_(1, 147)  \\\n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(1, 154)  Mechanism_(1, 155)  Mechanism_(101, 126)  \\\n",
       "1933581               False               False                 False   \n",
       "1933582               False               False                 False   \n",
       "1933583               False               False                 False   \n",
       "1933584               False               False                 False   \n",
       "1933585               False               False                 False   \n",
       "\n",
       "         Mechanism_(101, 162)  ...  Mechanism_(97, 128)  Mechanism_(97, 166)  \\\n",
       "1933581                 False  ...                False                False   \n",
       "1933582                 False  ...                False                False   \n",
       "1933583                 False  ...                False                False   \n",
       "1933584                 False  ...                False                False   \n",
       "1933585                 False  ...                False                False   \n",
       "\n",
       "         Mechanism_(97, 167)  Mechanism_(97, 181)  Mechanism_(97, 220)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(97, 221)  Mechanism_(97, 338)  Mechanism_(97, 386)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(97, 404)  Mechanism_(97, 452)  \n",
       "1933581                False                False  \n",
       "1933582                False                False  \n",
       "1933583                False                False  \n",
       "1933584                False                False  \n",
       "1933585                False                False  \n",
       "\n",
       "[5 rows x 456 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df_oh = pd.get_dummies(y_df, columns = ['Mechanism']) \n",
    "y_df_oh.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 456)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df_oh.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.where(y_df_oh, 1, 0)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_df, X_test_df, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.05, random_state=37)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data has 1836906 observation with 240 features\n",
      "test data has 96680 observation with 240 features\n"
     ]
    }
   ],
   "source": [
    "print('training data has %d observation with %d features'% X_train_df.shape)\n",
    "print('test data has %d observation with %d features'% X_test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1459100</th>\n",
       "      <td>0.666666</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.561708</td>\n",
       "      <td>0.553785</td>\n",
       "      <td>0.547734</td>\n",
       "      <td>0.540914</td>\n",
       "      <td>0.535847</td>\n",
       "      <td>0.531923</td>\n",
       "      <td>0.528790</td>\n",
       "      <td>0.524104</td>\n",
       "      <td>0.520750</td>\n",
       "      <td>0.516260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896046</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833330</td>\n",
       "      <td>0.833298</td>\n",
       "      <td>0.832626</td>\n",
       "      <td>0.831924</td>\n",
       "      <td>0.831228</td>\n",
       "      <td>0.829172</td>\n",
       "      <td>0.827164</td>\n",
       "      <td>0.825202</td>\n",
       "      <td>0.823285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968160</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333297</td>\n",
       "      <td>0.332970</td>\n",
       "      <td>0.326250</td>\n",
       "      <td>0.319520</td>\n",
       "      <td>0.313114</td>\n",
       "      <td>0.295610</td>\n",
       "      <td>0.280275</td>\n",
       "      <td>0.266704</td>\n",
       "      <td>0.254590</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069588</td>\n",
       "      <td>0.063861</td>\n",
       "      <td>0.058443</td>\n",
       "      <td>0.051457</td>\n",
       "      <td>0.045791</td>\n",
       "      <td>0.041188</td>\n",
       "      <td>0.037404</td>\n",
       "      <td>0.031587</td>\n",
       "      <td>0.027340</td>\n",
       "      <td>0.021568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605448</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000006</td>\n",
       "      <td>0.000006</td>\n",
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       "      <td>0.000003</td>\n",
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       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
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       "      <td>0.000001</td>\n",
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       "    <tr>\n",
       "      <th>1574931</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666148</td>\n",
       "      <td>0.661630</td>\n",
       "      <td>0.603996</td>\n",
       "      <td>0.578012</td>\n",
       "      <td>0.563949</td>\n",
       "      <td>0.544214</td>\n",
       "      <td>0.535572</td>\n",
       "      <td>0.530625</td>\n",
       "      <td>0.527411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065278</td>\n",
       "      <td>0.065304</td>\n",
       "      <td>0.065319</td>\n",
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       "      <td>0.065347</td>\n",
       "      <td>0.065355</td>\n",
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       "      <td>0.065369</td>\n",
       "      <td>0.065375</td>\n",
       "      <td>0.065381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "1459100  0.666666  0.000009  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.833333  0.833330  0.833298  0.832626  0.831924  0.831228  0.829172   \n",
       "968160   0.333333  0.333297  0.332970  0.326250  0.319520  0.313114  0.295610   \n",
       "605448   0.333333  0.003058  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.666667  0.666148  0.661630  0.603996  0.578012  0.563949  0.544214   \n",
       "\n",
       "         cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  \\\n",
       "1459100  0.000000  0.000000  0.000000  ...     0.561708     0.553785   \n",
       "1896046  0.827164  0.825202  0.823285  ...     0.000009     0.000009   \n",
       "968160   0.280275  0.266704  0.254590  ...     0.069588     0.063861   \n",
       "605448   0.000000  0.000000  0.000000  ...     0.000006     0.000006   \n",
       "1574931  0.535572  0.530625  0.527411  ...     0.065278     0.065304   \n",
       "\n",
       "         cINT1_3600s  cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  \\\n",
       "1459100     0.547734     0.540914     0.535847     0.531923     0.528790   \n",
       "1896046     0.000008     0.000008     0.000008     0.000008     0.000008   \n",
       "968160      0.058443     0.051457     0.045791     0.041188     0.037404   \n",
       "605448      0.000004     0.000003     0.000003     0.000003     0.000002   \n",
       "1574931     0.065319     0.065338     0.065347     0.065355     0.065362   \n",
       "\n",
       "         cINT1_9000s  cINT1_10800s  cINT1_14400s  \n",
       "1459100     0.524104      0.520750      0.516260  \n",
       "1896046     0.000007      0.000007      0.000007  \n",
       "968160      0.031587      0.027340      0.021568  \n",
       "605448      0.000002      0.000001      0.000001  \n",
       "1574931     0.065369      0.065375      0.065381  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.66666375e-01, 8.64659390e-06, 0.00000000e+00, ...,\n",
       "        5.24103567e-01, 5.20749569e-01, 5.16260321e-01],\n",
       "       [8.33333333e-01, 8.33329783e-01, 8.33297833e-01, ...,\n",
       "        7.39333964e-06, 7.19593722e-06, 6.89835126e-06],\n",
       "       [3.33333333e-01, 3.33296993e-01, 3.32970356e-01, ...,\n",
       "        3.15867918e-02, 2.73400487e-02, 2.15682561e-02],\n",
       "       ...,\n",
       "       [3.33333333e-01, 3.33324903e-01, 3.33249064e-01, ...,\n",
       "        8.90973969e-02, 9.32020928e-02, 9.88450912e-02],\n",
       "       [3.33333333e-01, 3.33322224e-01, 3.33222288e-01, ...,\n",
       "        1.10472690e-01, 1.14783402e-01, 1.20264515e-01],\n",
       "       [3.33333333e-01, 3.31398102e-01, 3.18951029e-01, ...,\n",
       "        7.26743944e-06, 6.06035491e-06, 4.55024782e-06]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train_df.to_numpy()\n",
    "X_test = X_test_df.to_numpy()\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[412 245 128 ...  62 136 400]\n"
     ]
    }
   ],
   "source": [
    "# Find the indices of '1' in each row\n",
    "indices_train = np.argmax(y_train, axis=1)\n",
    "\n",
    "# Create a 1D array with values corresponding to the positions of '1'\n",
    "y_train_1D = indices_train + 1  # Adding 1 to make the index 1-based\n",
    "\n",
    "# Print the result\n",
    "print(y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[306 402  60 ... 131 132 154]\n"
     ]
    }
   ],
   "source": [
    "# Find the indices of '1' in each row\n",
    "indices_test = np.argmax(y_test, axis=1)\n",
    "\n",
    "# Create a 1D array with values corresponding to the positions of '1'\n",
    "y_test_1D = indices_test + 1  # Adding 1 to make the index 1-based\n",
    "\n",
    "# Print the result\n",
    "print(y_test_1D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Train RF Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_RF = RandomForestClassifier(n_estimators=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=120)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=120)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=120)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifier_RF.fit(X_train, y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9996352562406569"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifier_RF.score(X_train,y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_predict_1D = classifier_RF.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5236346710798511"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifier_RF.score(X_test,y_test_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8iYEDB6oK8nrCYAcdS3aTk5MZ7BAREUUZX1NQOEGZiIiIYhqDHSIiIoppDHaIiIgopjHYISIiopjGYIeIiIhiGoMdIiIiimkMdoiIiCimMdghIiKimMZgh4iIiGIagx0iIiKKaQx2iIiIKKYx2CEiIqKYxmCHiIiIYhqrnodZRXU9yqtqUTg+CzPGZPjcb1RGP2yvP4EGi9XjfjqNgG+eu76nmktERKRSUV2PJev2AwAGpZyDmoZmr/vWlZlD1SwVBjthVl5ViwaLFeVVtV6DnYrqehS/uxt2EV6DHInNIfZEM3vc3BU7UFnTiL6JcbBY25FjMmD1nCvD3SwiIvLh2cq9sNocAABLJ4FOOHEYK8wKx2fBZNSjcHyW133Kq2phFwGt4Pt4Oo0fO0WgNbsaYRcBi7UdADq9MiAiosjRejbQiWTs2QmzGWMy8MWB4yh+dzfKq/6DBkur/JhWAMw56Sgcn4VnK/ei1eaAUR8nBwQAYNTH4fH8i/0aCotkiTotrDY7BAAigByTIdxNIiIiPxhcvpciEYOdMCtYtlnuxVAGOgBgF4HVuxoBQO4iPNXa7nHMM1qCHOUcJQDy7/PNw6I+YCMi6m0yiyrD3QS/MNgJM3+GayprGuXfzTnpAPyf2BxplHOUAMi/bym6JqreBxERRQ/O2QmzzoZrtAJQkJsOc066/PvSaSMBuAcN0UI5R8mf+UpERBQbwjmnVBBFMTqX7wTRyZMnYTAY0NzcjOTk5JC//riyjWiwWKEVnENXks6W6EVrz06skYYhpdVjrvfzStbBYm2HUR+HncX5qi7fcC3BJKLI5XpuH1e2AQ2WVpiMiQA6pjsIAA4EcA5RnpOU82xyTAbVSIPJmOg2tSJQOo2A1OTEHvm+8vf7m8EOwh/suObQYQATOt0NGpXBi+vJQ6cROk0FwGCHiFxJF78mox5biq7pdE6Mp3OIlMbDnOMcCXA9x4Vyjo1Rr0NSQlyPfqcx2OmCcAc7FD6uJ5auUvbkKK+KXO97Ig3hMbAlIokUnLS02WCxtkMjAA4R0Os08kIVwL1nR3peo8UKEc5pELWlZvkcJx0nVEJ1fvP3+5sTlKlXKxyfpVod1lXKxIfK7uHVc67EkF+977VnRwB8JpMkot5D2QOj7NFxiM7zhTLQ0QjAMzdlq54vzePU6zQ40+5AmiERWfMqMSLdOS/UV0La7or0nmpOUKZebcaYjIBXglVU12Nc2UZUVNcDAB7Pvxgmox6P518MAEhK0Hp9rghwcjYRyaRgZcm6/RhXthHKqbyul0wOEW6LU6QFH/PNw1FbakZTcyvsIrCnsTmgXutYw2CHKECuK+Jc77smf1Qy6uO43J6IZFKwAjh7YRJ17hdLApyrcj1dKLleuEk9OtJtb0/UymCHopZrz0ogfjSvEplFlfjRPGeXcV7JOmQWVSKvZJ3P547K6AfAeWLKLKpES1u76r5EWomlXnYpdKvdRBRbpGDl8fyhMBn1SIhz/3peNCUbq3c1osFixVOrdns8jnRePHj8BwDOXG6ZRZX45ugp1JWZUZCbLu+r9xBQxSoGOxS1uptraO6KHfKEPYcIDPnV+3JvjD+pz9d+dVh132K1edzvqotSAaiHtSxWW9TlSCKi4PN20XbVRf3d9vUW4CiVrN6DBosVJ1ttck8R0DHnZ+m0kVg8JdtrQBWrOEGZolZ3JxcrM1NrBHXFeH9SX/lbYb6yphFLp410GdbScb4OUS/WseqqHRarDc9W7kXxu7sRH+dcddXVCcXDFqxVTWJ2iEBTc+fHuOqi/nJJoljXe8I6ijndmVwMQJWZ+ttSs2qY6RI/xreVY+CumUE1Z48rFXMFOubtOIe1JnG+DlEvJvVMA87FCq02B+yis4K41o+rLdd5gFYPlceVSWqV+0uvvb3+RGCNdxG+vMj+Y7BDvdbowSlIM+gxenAKAGDY+R05GvY0+q5Zlnlekvy7zSFCr+v4c3KIzu7i2lKzXOLDdbUWEfVe0oTkx/OHYkvRNbjx7MXRjbnpKHFZVu6J61C78vwDOC/AtELHvJykBJ3bawerdzkakvUx2KFey3XOjzIJoNQb0xnlMJhOI6iurFxPPJ5ej4h6L9eeaeXF11vbDvl8vlQ+QjLfPBwmo14R3MShttSM+eZhboFNd3vFXYWx5JXfOGeHei3XOT9SLRiTMVHujemMOSddlZZdSiKo0wjYt2iyx9dbsm4/WtraUVFdz2EsIpLLO0hzdZas2+91sYPEZEzElqIJqm3KYTEA8upQaVLzU6t291i5iBv8uDgMNwY71GvNGJPhEnAILrdO0sloRLoBx1rO4HCzFQ7R2ZtTW9qRNTQ1ORENFitSk9VXXMrXU/buMNgh6t0qquvlCcKtNgeMeh2aXQIdkzERozJS5P2cObomuB2rcHyWarWWzSG6BTaBBDq+avwBHYswIhmDHaKzvK3uqqxphF2EW60rm0NU9dD4szqsuyvIiCj6zV2xA2t2NarmughCR/oKrQCU3JStuiDqLJjorDSNv6RyD66FqZW3n/z7O7S0tbu9lj/D/uHGQqBgIVDqnLJnZ09js2qFQ6AFRImo98qaVymfR7QC0DdRpxq6Wjwlu0s9v8EYmpKCHalwqFSxXFoaL92XCo0G2tZg8/f7mxOUiXyQVlXdftlA9E3UQa/TQK/TMFcOEQXEnJMOAc6FDCU3ZePx/KHQ6zRyOYiuBg+uqS+6w7VsBYBOy1hEy4ILBjtEfiqvqoXFasOZdgesNgeSEuI474aIumzptJE4UGbGvkWT5XNISlICFk3JDmjuS3HBCLdcN55WhHbFVRf1h8mox1UX9UdLWzva2u0w6nWYbx6GujKznIU5Wi74OGeHSEEary4cn+UWyEjzbaQxbNc/cmm4S1qdRUTkS8GyzfJ8wPKqWixYtRsinMskFk3JVp2PvJ1jyqtq3XLdeEoy2JmK6nosWbcfzVYbRADHW9rcMjmnJCXI58UvDhxHU7MVXxw4HhUXfQx2qNeQAplzk+Kxp7HZY1CiXC21ZN3Xbom7BADnJsV7PL40kTkaViYQUfjNXbFDtfBBGViIgNvqTW/nmMLxWXKQFCip5xpwziNqdQmWXIfto+18x2Es6jWkE0dNQ7P8R+pKmVnUUzFQEc5VWZ6SA0rlJ6JhZQIRhU9FdT3ySj70WZeqpa0dRr0O5ybFI2teJdIMiR7PMU91M9ABnOc+o14HnUaAQwTSFUkLC3LT3UrcRNv5jsEO9RpSIJNjMnj8I3UdwnKtPQM4e3ZyTAY5IFJWLHYtD0FE5ImyF8Ubzdml6Kdabfjq7AVaU3Nrj5xjpEnRO4snwSGKEAE0Wlph1Otg1OswenCKW3X20YNT0DdRh0/+/Z1bxfZIxGCHep3bLxvo8YThWs7hqotSoRU6VjoY9XE4UGbG6jlXyqnWWQKCiLpK6kXpbBKxQ3QOJ9lFIPHsSq34OE2PBBardzUir+RDVFTXY0S6s8BxnFaAxWqDxWpDeVWt27lOCtikxyMdgx3qNXwFJq7F8aQxaSmBlqdhrWAX1COi2PfFgeM41eq7JETJTc4VT/PNw5Fu1MNqc/RYYCEFLcdazgAAkuLj5J6dwvFZbuc6KWCLlhQcTCqI8CQV7GzVD/WMrv6bSysfNIIzXbpRH4edxfkhaCkRxTIpqaAAIN2olxP3SQpy3RdPuJ6/fjSvEt1MmgzAOSx/8PgPAIDH84cCQFR9N/n7/c1gB6EPdiqq6+UaJhoB+FZRX4mIiGKb6xLyQC7EvE1uriszqzIq15WZVUWKv3nueuSVrIPF2h4TF3AMdrogFMGO8j+zslgb0JGmm4iIyBdv5SF643eJv9/fzLMTIt7mi5iMnitkU2goTxqLp2SrAlF/ThyuV2jjyjagwdIKkzHRY2ViDl8SkZLrOUTZCwPA7wKf3kpG+JvsVDniICU07Ox8GG3nMvbsILw9O70xEg8VX3+MUleuN74+G2XmU60ApBkS0WBp7fT5UpE9FhAlIqD7RTxdC3G6Vi2XCndqBaDWy5SJrrShIDcd2+uPy+c6TxXaQ4k9OxFmxpgM+T9DNETBscA1+6irzgIdfygzn5pz0lVj6N567KSSE9GweoGIIpunCyrpvCdlY9ae7fDxlvxv7oodXXpN17lCdhFez7GRhEvPKWb5WhauTBro2gWsEaBKoOVJjsmgui8FOFoBKBx/ocfnzBiTIefoISIqyE2XA5JgUFYtB5z5ejpLROgpk7y/9DoNtAIwKqNfwMcIlYgKdsrKyiAIAh5++GF5W2trK2bNmoVzzz0Xffr0wdSpU3HkyBHV8w4ePAiz2YxzzjkHqampeOKJJ9De3r2rdop+vgKLlja7/LvNIap6YxwifCYLvP2ygfLvlTWNGJWRAqDjSseVawZSIiIp83qw529KmeIvMRk6Pe8EWu4hx2TAmXYH7CKwvf5Ed5oaEhET7Gzbtg1//OMfkZOTo9r+yCOPYM2aNVi5ciU2bdqExsZG3HLLLfLjdrsdZrMZZ86cwWeffYbXXnsNy5cvx8KFC0P9FijKKCf+6XUa1XwbjQCfyQKVAY05J111heTpecy2TETeFI6/ECajHroAu3mki6kl6/ajwWLFsZYzqC0141jLmaCed7SCc57QsZYzsItST3bkD8tHxJyd06dPY/r06fjzn/+MxYsXy9ubm5vxl7/8BW+88QauucY5mfPVV1/FsGHDUF1djTFjxuDDDz/E3r178dFHH2HAgAHIy8vDokWL8OSTT+Lpp59GfLznCtVEApyFPQUA+xZN9rmSypVy/s2MMRmo+74FNQ3NXldFcL4OEbkKdIKy62lGupiSHD3pvHiTtikfU/I1jCWdJwFAr9NivnmY3FseTauxIiLYmTVrFsxmMyZOnKgKdrZv3w6bzYaJEyfK2y6++GIMGjQIW7duxZgxY7B161ZccsklGDBggLxPfn4+CgsLsWfPHowc6T5O2dbWhra2Nvn+yZMne+idUSSRlmDaXdYfSicN1wBHWtUAiGiwtCLHZMDqOVeqVnkpV1RJadZtDtHjhD3lJHUi6t26uwrLfUW6eoPNIfr1GuacdKzZ1YhEnQbzzcOxZN3X8uKNglznEJenpevRdj4Le7Dz5ptv4l//+he2bdvm9lhTUxPi4+NhNBpV2wcMGICmpiZ5H2WgIz0uPeZJaWkpSkpKgtB6iiaeAh1P1c8lrldK0uorb6u8CsdnYcm6/fLvRESedDfQAaAqIjp3xQ7VMLwvrrl3XIMYV8Gush4OYZ2zc+jQITz00EP4+9//jsTE0CXXmzdvHpqbm+WfQ4cOhey1KXzSDO7/x9IMeowenOJxf2lVg7RqS5pAOCqjH7QCcG5SPPJKPpSrBc8Yk4GdxZOws3hSVF3xEFFk8jIijhyTAfsWTZbvK4eivD1n8ZRs1f52sXsrsaJNWIOd7du34+jRo/jxj3+MuLg4xMXFYdOmTVi6dCni4uIwYMAAnDlzBhaLRfW8I0eOIC0tDQCQlpbmtjpLui/t4yohIQHJycmqH4p9Tc3uVz6dTdyTVnMlJejObnGeRbbXn4BdBPY0NsNitcnVgomIgkk5VKWct6zM8QWoe6dd0wRLcwhdF1Qoe7V7w0rRsAY7EyZMwFdffYWdO3fKP5deeimmT58u/67T6bBhwwb5Ofv378fBgwcxduxYAMDYsWPx1Vdf4ejRo/I+69evR3JyMoYPHx7y90SRS/oDl7p/jfo4nyuuAPd8PdJ9c046jHodjHodh62IqEfZxY7AxTXH1yf/7vj+E6Hu3XGm1VCf56Tl7tLwVG9YKRpx5SLGjx+PvLw8vPjiiwCAwsJCvP/++1i+fDmSk5MxZ84cAMBnn30GwLn0PC8vD+np6Xj++efR1NSEn/3sZ7j//vvx3HPP+fWaPV0uoqurfIiIKPb4O1dn8ZRsef7f4/lDAXS+8kl5XKM+TpUdXlpY0Zloq3OlFDPlIl544QVoNBpMnToVbW1tyM/Px8svvyw/rtVq8d5776GwsBBjx45FUlISZs6ciWeeeSaMrVaTJo51ZQJZT+nqf2qpfpRRH4edxflyPSjpD0hZtG7Y+cmq7lXW/SKicFFeZBaOv1CuSajTCPjmuevdJum6nhtdL1KVAUWOyeA2lBSoujKz6tjSedPTak5vpABHOk8r2+4r0JGOHW1BTldFXM9OOPSWnp2K6nosWLUbIgCjXoedxZN8Pkf5R1iQm+5WF6UzDHaIKFyU5y6TUa9aWekpwMgr+RAWq00+N7o+HowVVK54juw+f7+/GewgNFXPI4FUcRvwP9hR9uycam13W7rdGf4hE1GwufbAKO8DwMJ3d8MhOuetuOeicS4z0AjwmG/LIaqT6PUUnhuDJ2aGsah7lF21yjww0jiwLzuL892OlWZIRFNzq+pkodMIqvILwa7zQkQEuOe5cp1cK52GPAU6JqMejRarx4s2x9nSB3aXFVBducCjyMVgJ8Yp8yksnTayW+Oyrsmnhi34AFabs5imzSFCr9PK9yNhfhIRxR7Xsiuu96WeHVc6jYAGixV6nQZn2h2Ij9PAanPIF2pSb4/Us2PUxyEpQYejJ1tVF3IUnRjsxDipQGWglW0702rrqBouuNz3ltiKiKg7XCfTeppcW15Vi3OT4lWTiIedn4xjLWfchr+kcjASw9khfmnYX1ru7W1YjKIDg50I190lgZ/8+yjsovNWPUE5TjVE5c3gokp5/Np1kp9e57wyAoAbz9ZQUdZYISIKFelc2dLWDovV5lb4Ugp8nlq1GzPGZOCLA8fR1NwxpNURyDh/kXqMpOMw0IluYU0qSL51J9lTwbLNcr4Fi7UdT50NdKT7vmQqAh3AvWquFOgAzqzCdd+3QAQwJLVvzC9jJKLIsmTdfjRYrGhrt6vqRnmzepd7rTyg49woZVB3TeBH0YnBToRzzd7bFcouXKm+kySYo0xawdlO6fWClX+CiMjV3BU7kDWvEnNX7ADgvKjLLKrEyVYbAOdFmPJCzJOseZ0vI1ce//bLBsJk1Aeh5RRODHYinHR1EUhPifKKJCkhTnW1Iw07+cvT6qockwEmox4lN2VjxpgM+fV0GiGma6wQUfi4FrGULq66MszkbYWVyaiHcPZx6fhS77ouSBMROZ8xPBjsxDBl5swGSyvmm4fLxeS215/w+XxlcORpddWxljOqQGz1nCthMuphc4gxXWOFiMJHqnE3It2AcWUbA0pzoRWcSVLrysxyr7d0vrvEZFAVyZR61ydfcn5Q2s+5P+HBYCfGSScCkzER5VW1sIsdw06+KLuCPZ1QPB2jO8NuRES+SEUsj7WcOTuPUEBdmdnvuTU5JoOqCGZSgjPYOdPuQIPFimMtZ1SPSz7593dBfR8UWlyNFeOU5Sm6urJLWm2l12mwpWgChi1YK9/ft2iy2/7RXEyOiKLLqIx+aGq2YlRGPwDOuTW+5gsa9XFyj7dr+QdpaKvBYlVlmw82JlwND5aLQO8pF+GNFKScmxSPPY3NEOHsavUW1HgjnSBMRj22FF3Tcw0mol5POt/odVq02uxuJR6M+ji0tXdMVtYIwLelPVPjqqtYLiJ4/P3+5jAWyRPwahqaYRc7xpR9rWhwxSEsIgqVwvFZ0AqAVRHoaAVg8ZRs1JWZsbM4HylJCQCcE4+/LTWjYNnmoLz24inZMOp1MOp1WDwlW76vpBU8r3rlUvbwYLBDcpCSc3ZinrRawFuuiorqeowr28gVV0QUNjPGZKhWVel1GnllqMT1AiyYaTGSEuLweP5QOYPzzuJJ8hCV5uwE50TFOVSnEbB4SrZq4QiFDoOdXqyiuh55JR9iybr9KByfhdsvG4g0gx7nG5x/sENS+3p8nrdEh91JgEhE1FXK4GK+ebjbXEHX1B3B6lXxdq7bUjQBi6dk43yDHp/8+ztYbQ4IcPbwcJVqeDHY6cXKq2phsdpgsdpQXlUr/wFLy8y9XQV5G67iMBYR9STXXuXC8RdCe7ZmlT+BROZ5SUFpR2fnOmWJCa3gLD4hwv9VsNQzGOxEuJ4cMiocnyWPOxeOz5L/gKWrJaM+DoOLKjFswVrV60tXSwA4nEVEIaPsUSlYthlPrdotp9NosFiRWVTpcQKydB5ds6sxKO14a9shNFiseGvbIfn4eSUfIq/kQ4zK6CfP3zHnpMOo18mJCsur/hOU16eu42osRPZqrK6scHJdVWXOSXfLFaGUV7LOrUZWXZkZc1fskLOUSjy9vmvbuBqLiHpSRXU9lqzbj2arzW31lZK02sm1OKiyeHFPkUpLNFisMOp1SEqIUy1j50qs4OJqrBjRlaEh11VVUrpzb7wVA1UGOgKcE//8SSDIYSwi6klvbTsEi49AR0k5pGQy6oMW6HgqHSEAbr3kgDPokXZnjp3wYVLBCCfN9PdH4fgst56dzhj1cR4DHnNOOiprGt16hlyTBrq2rSttJSLqKn9WUykDCumcKF2APbVqd7fboNdpMd88DDPGZMi94wKARVM6VoLNXbEDTc1WjEg3IKnlDBOtRgAOYyGyh7EC4W8mY2m4Ks2QiKbmVp/DXhymIqJwKli22a+Ax9NQUVezIguAxx4krQDUlnoeipLOvY0WK8SzxzCcnb8jLVOn4OIwVi/m7xJwabiqwdLq17AXh6mIKJxuv2ygz328DRV19byV6CXPWGc95tK5N1GngVZwHkO54pXChz07iO2enYXv7oZD9JwqvSA33WfPjvJYQEc3sADgQJlZvtLKMRmYLIuIepQ/vTPBmISsFYCSm7JR/O5u1UKNgtyOc+S4sg1osLTCZEzElqIJqnPr4inZ+OLAcazZ1Yg4jYA4rQaAiIQ4LXt4gszf72/O2YlB0tyZYQvWyqUfHKJ74bvRg1M6HbaqqK6X/9jLq2px9GSr/Jj09y91KQczMykRkSeF47OwZN1+AIDFavO4j7+BzuIp2ao5PDkmA2oamqHTCCguGIEZYzLwxYHj8vzF0YNTUF5V63YebbC0elzuvvrsMnebQ4TNYZfbVl5Vy2AnDDiMFcN8/dH76lYtr6qVc1gUjs+CzdFxiSOtRZAykrLeCxH1tBljMuRl5N1VXlWLujKzPOwlXbDZHKIcjCydNhK1pWYsnTZStbLLlwVeJkILYGLBcGGwE8O81bYC/PujKxyfBZ1GgF10LvlUBjSXnP199ZwrUVdm5hAWEYWE8qJL66nSph+Ucw+ljPESbxduXQlSPM0NcV2xRaHFYCeGuda2Uk7c82ei1owxGfKJpaahWRXQcNiKiMJBeRFnFzsKF3tIfeOVsl6WdF40GRM7vXCbMSbD7zw5ep1GVfFcr9Mg/WzeHQoPBjsxTBmQ6DSC2xWMP6sDXIepOGxFROHkOjyfEKeByahHQlxgX2dbiiagrsyMLUUTfO57blKCX8dMSUrAjbnpZ5OyapEQp2WR5DDjBOUYMuRX76u6eCVGfRx2FuerHpcyfQIdZSOk/ZQrsJRXORXV9TjWcgaL2RVLRBHCanN0KX+OUR+HuSt2yBOITcbEs/OA2uXHXc+DAPyas6PTCEhNTpRznG2vP4EGi1UOyDhfJ3zYsxNDPAU6AHCqtd3t8aSEODlgkf7IpVtveXr8zd9DRBSpLNZ2OdABnHN2lJnkXc+Dz1buxVOrdruVfagrM7vNi3SIomqITMpN9nj+UNV2Cj0GOzHEU70WoCMJljT0pNMIqisMoz5OdesteSCTChJROHla4u1JQW56wJOXledBo16nGjZLTtSd/c158H2LJqOuzCy/nmvCwRljMhjkRAgOY8WQYecno6ah+Wy3rB1t7XYkxGkxenAKAMhDUlL37BcHjmN7/Qk8nn9xUGpc+VumgoioJy2dNhJLp410G9pXJhw0GRPd5jECzl5vyanWjiXuBbkduXZcL/ik16PIxZ6dGCJNSHZ2y9pgtTk8pimXumcraxq7NCzlaxiLw1xEFAkKlm0GAHzz3PWoKzPLvTytNgdMRj0WT8lGU7N7oAN0LEVX5hlbPCXbazBTUV2PcWUbUVFdH/w3QkHDYCeGSMNUJmMijHod9DqNaiKyRBqOMuekd2lYytcwFoe5iCgSuKbGMOc4h5nSjYloarbiiwPHvda4kpaXS+ezkps6FmR4uqDjRV50YG0sxF5trEjEIS4iCkRFdb2qrENnpJIP3mr1Zc2rlHtral1qBfpT38/TeYzntvBibSyKKMqrH54QiMgXqdCmN3Vl6mClrszs85jmnHS51hXgOzhy5Wk+Y6BzHCm02LMD9uyEAq9+iKgrOlt55U9gQ70De3YoovDqh8g/sXZhoEzgJyXsA5zv89nKvX5XKZcwezsFgsEOEVGYuAY2yqGbWBjyraiuVyXwUybvK6+q7VKgYzIm+lXSgcgTBjtERGHiOpdNOUelKyUQItWSdftV99V5/vyfQcFhK+ouBjtERCE0d8UOeZLsqIx+aLRYcbylLebytFRU16PZalNtU4Y3nU0+VmKgQ8HAYIeIKIQqaxphF5238XEaiHAWs4y1PC3lVbVd6Lsh6llMKkhEFCQFyzYjs6gSmUWVGLZgrcd9pAR35px01ZwV12GrglzPSe8iSWfZg2NhGI5iB4MdIqIgUWbutdocHoOBpdNGorbU7LOWUjTUWvKWPTivZJ3X50jBoD+kopxE3cVgh4goQK7BjHJZtF6nwZJ1+9FgscoTdaUveunL3rUwt/T8aFle7alETMGyzapVVwCgEboeuNSVmeVl6kTdxbCZiChAS9bth8Vqw5J1+zFjTIZbFt4fzXMGNSdbbZ6ejgNRPvnWU/4sZe+WgI736K03hxOQKRQY7BARBZFytZXj7AxdRy+aqavTCrDZnW9YROeZkAH32leLp2SjZPUe2Byi32UciHzhMBYRUYAezx8Kk1GPx/OHAnAO4aze1bHaynVYavGUbJiMeiyekh22Nve04htHQOM6PueFTiO4Fflcsm4/bGejQ9fq5USBYm0ssDYWEQWHay9GXZm50/IPylIKJmMiCsdfGDOlInwNWynfuzfs2SFf/P3+Zs8OEVGAOlt6LfG2Yglw9v5IGiytne4bTTobusosqsSQX73faaCjFZy9YAx0KFgY7BARBWjhu7vRYLFi4bu7PT5eUV2PlrZ2GPU61YoliTmnI5eOs2fHfXVTtPEW6Oh1HV83NpdJTAI6hvp0WgElN2VHfc8WRRYOY4HDWEQUGOUXu6dVRePKNqLBYoXJqMeWomtC2bSgUA7BLVn3NSzWdug0AhyiCI0guAUtntR5WY2lFcCghrqNw1hERD3MZExU3bqK9p4aZZ4gKXeOzSHCLrr3zniiDAClnhuTMREmo56BDoUUe3bAnh0iil6dTYDurrySD2Gx2qDXaVSlLfzB/DkUCuzZISLqBXp2UrPzWrirgQ5RpGGwQ0QUxXpyqMy17ANRtGIGZSKiKOapZEOwGPVxDHgoJjDYISIij5q7GOhwhRVFKg5jERGRR11dvWIXgadW7UZeyboeaQ9RoBjsEBGRm86yQvvCoS+KNAx2iIjITXdWdxn1nCFBkYXBDhERuWmwWLu0v7Ki+87i/B5qFVFgGH4TUa/kKxlfRXU9Slbvgc0hQqcRUFwwIion3s5dsQNrdjUiUafBfPNw+T1UVNejZM0e2Oz+zcwRAGgEIM2QiKbmVphz0lH3fQtqGpqRYzL06Kowou4Kawbl8vJylJeXo66uDgAwYsQILFy4EJMnTwYAtLa24rHHHsObb76JtrY25Ofn4+WXX8aAAQPkYxw8eBCFhYX4+OOP0adPH8ycOROlpaWIi/M/jmMGZaLeR6pbZdTrkJQQJwc9UhDU0tYOi9Um768RgG9LoycrcEV1PRa+uxvKqg7KGl2Diyr9noAs5fFhMEORxt/v77D27FxwwQUoKyvDkCFDIIoiXnvtNdx0003YsWMHRowYgUceeQSVlZVYuXIlDAYDZs+ejVtuuQVbtmwBANjtdpjNZqSlpeGzzz7D4cOHcdddd0Gn0+G5554L51sjoghXOD5LDmqkDMRvbTuEmoZmAIBRr1Pt70cpqIhSsnqPqs16nUaVeNDft8OyDxQLIq42VkpKCn7zm9/g1ltvRf/+/fHGG2/g1ltvBQB8/fXXGDZsGLZu3YoxY8Zg7dq1uOGGG9DY2Cj39rzyyit48skn8d133yE+Pt6v12TPDlFs8qdulHKfp1btlrfnmAxy4AM4J91G01wU1yrjEm9VyL1hsEORLOg9O7fccovfL/7Pf/7T730ldrsdK1euREtLC8aOHYvt27fDZrNh4sSJ8j4XX3wxBg0aJAc7W7duxSWXXKIa1srPz0dhYSH27NmDkSNHenyttrY2tLW1yfdPnjzZ5fYSUeST6kY9tWo3yqv+gy1FEzB3xQ5U1jTCnJOOpdNGquaaKIMdZaDjJISw5d3H7MdEHfxejWUwGOSf5ORkbNiwAV9++aX8+Pbt27FhwwYYDIYuNeCrr75Cnz59kJCQgF/+8pd45513MHz4cDQ1NSE+Ph5Go1G1/4ABA9DU1AQAaGpqUgU60uPSY96Ulpaq3s/AgQO71GYiilwV1fUYV7YRFdX1OHqyVd7eYHH+XlnTCLvovPXFZEzssXb2NAY6RB387tl59dVX5d+ffPJJ3H777XjllVeg1WoBOHtmHnzwwS4PAw0dOhQ7d+5Ec3Mz3n77bcycORObNm3q0jG6at68eXj00Ufl+ydPnmTAQxQjlFXAbYpJKxrBGQjFx2nRarPDnJOOgmWb5dVEq+dc6XaspuaOYOnx/KEhaX8wFCzb7LZNr9PAanP4PXzVU8VFicIhoDw7f/3rX/H444/LgQ4AaLVaPProo/jrX//apWPFx8fjwgsvxKhRo1BaWorc3Fz8/ve/R1paGs6cOQOLxaLa/8iRI0hLSwMApKWl4ciRI26PS495k5CQgOTkZNUPEcUGZRVwva7jFPfMTdkor6qF1WZHulGPpdNGykNV0q20v16nQV2ZGeacdGgFoCA3PapWIimH4HJMBtSVmWG1ObzuX5CbrhqkK8hNx5aia6LqPRN1JqDVWO3t7fj6668xdKj6Sufrr7+Gw+H9D8ofDocDbW1tGDVqFHQ6HTZs2ICpU6cCAPbv34+DBw9i7NixAICxY8fi2WefxdGjR5GamgoAWL9+PZKTkzF8+PButYOIoo+yp8ZT3pe3th1Cg8WKc5OcixekScg5Jufw+75Fk1X7L502EkuneZ77F6nmrtihuu+px0pJmoAcbe+TqCsCCnbuuece3HfffaitrcXo0aMBAJ9//jnKyspwzz33+H2cefPmYfLkyRg0aBBOnTqFN954A1VVVVi3bh0MBgPuu+8+PProo0hJSUFycjLmzJmDsWPHYsyYMQCASZMmYfjw4fjZz36G559/Hk1NTXjqqacwa9YsJCQkBPLWiCiKufbUVFTXY8Gq3RDhnLB7qtU5j2VPo/NxX4GAdAxfK7oiietcJF/DVj+aV6laoq7TCPLwn8mYiC1FE4LeRqJQCyjYWbJkCdLS0vDb3/4Whw8fBgCcf/75eOKJJ/DYY4/5fZyjR4/irrvuwuHDh2EwGJCTk4N169bh2muvBQC88MIL0Gg0mDp1qiqpoESr1eK9995DYWEhxo4di6SkJMycORPPPPNMIG+LiKKca0/Ns5V75XwyFms7CnLT5ZVY3riu1lLOAYqGYMecky5Pwu6MNIfHNX+Qcp6TNKmbKNp1O8+OtGw7mue9MM8OUezwljcH8J0zpqK6Xn6OVgBqS81R17Mj8ZVnx58MyuzZoUjX4xmU29vbUVVVhdraWtx5550AgMbGRiQnJ6NPnz6BHpaIKGAV1fUofnc37KJ71W5/suQonyP1/kRbzSdvQY5ep4XVZkdmUSW0gjqDskZwTuBesm4/AOfKs2h6z0S+BBTs1NfX47rrrsPBgwfR1taGa6+9Fn379sWvf/1rtLW14ZVXXgl2O4mIfCqvqoVddPbKuPbsHPAjE7BUQiISenE666HqqsVTslH8bscx7KIz+JPmMkmZocP9nol6SkDBzkMPPYRLL70Uu3btwrnnnitvv/nmm/HAAw8ErXFERF3hGqx09cs7knpxlqzbD4vVJve2BEoatvriwHGs3uWcvKwVgJKbsiPmvRL1tICCnU8//RSfffaZW+2pzMxMNDQ0BKVhRERdFUnBSri5zk+KxmX0RMESUFJBh8MBu93utv2///0v+vbt2+1GERH1do/nD4VWACxWW7ibQhT1Agp2Jk2ahBdffFG+LwgCTp8+jeLiYlx//fXBahsRUa/1xYHjPpePE5F/AhrG+u1vf4v8/HwMHz4cra2tuPPOO/HNN9/gvPPOw4oVK4LdRiKiXsefQqVE5J+Agp0LLrgAu3btwj/+8Q/s2rULp0+fxn333Yfp06dDr9cHu41ERL1O38Q4VeVyoz6OlcyJAhRQsPPJJ5/giiuuwPTp0zF9+nR5e3t7Oz755BNcddVVQWsgEVFv5BrYdCXQ8SenUFdFa3JFIiDAOTtXX301jh8/7ra9ubkZV199dbcbRUREgQv2VB8pWaNUNoMo2gQU7IiiCEFwv3Y4duwYkpKSut0oIqLerGDZ5i7tL7jcmoyJQW2Pa7JGomjTpWGsW265BYBz9dXdd9+tqixut9tRU1ODK664IrgtJCLqZaSq7f4S4bvuV1cpC6JGUmZpokB0KdgxGJyVhEVRRN++fVWTkePj4zFmzBhmUCaiXktZSFSnEfDNc52n4lDu3x06TfBn6UiV0ytrGrF02sigBzlzV+zAml2NSNRpkZKk81hhPcdkwOo5Vwb1dal36lKw8+qrrwJwZkp+4okncM455/RIo4iIopGytIPN4XnmTCA1r5zDUgJGZfTD9voTGJXRD+v3HoHVZodep8F88/BgNF/FnJMu9+z0hMqaRogArDY7GizuSWqBrvdwEXkjiKLY5blsBw4cQHt7O4YMGaLa/s0330Cn0yEzMzNY7QsJf0vEExF5MnfFDrnulJJyaEkKclra2v3OihzsoalIwp4dCgZ/v78DmqB8991347PPPnPb/vnnn+Puu+8O5JBERFHLnwSA5VW1aLBYQ9CayDFswVpkFlVi2IK1bo+NHpyi6NlxD3QAIPM8Lnih4Ago2NmxYwfGjRvntn3MmDHYuXNnd9tERBTx5q7Ygax5lZi7YgdGpBs87jOubCMKlm1G1rxKnJsUD5NRj6su6h/ilobHsAVrYbU5AEC+VfI0hGcy6pFj6vi3ZBZpCpaAgh1BEHDq1Cm37c3NzR4LhBIRxRrlBF5vc0saLFbUNDTDLgJfnd3nk39/F8pm9qiK6nqMK9uIiup6t8eUAY5e5/urRlrWrvy37Kn5QtT7BBTsXHXVVSgtLVUFNna7HaWlpbjySo6vElHsM+ekQysAaQb3nDaevtxFOIMff+frFORG/he9NDTnKdGg9G+g12mwb9Fkt8elFWQawdmjMyLdgOJ3d8vbjfo4LJ02sgdbT71JQOUifv3rX+Oqq67C0KFD8f/+3/8DAHz66ac4efIkNm7cGNQGEhFFotGDU7C9/gQaXebhSJOKM4sq/TqOyZjoNmfFZNRHxRe9Mv+OK08BTkV1PUpW74HNIbpNPs6aVwm7CNjPrpk51co6YBQ8AQU7w4cPR01NDf7whz9g165d0Ov1uOuuuzB79mykpKQEu41ERBFFKp9gF509F2faHTDnpMsBytwVO/w6jutqK+Wy9GgwY0xGl/LvlFfVykvyXYf+pKXuaYZENDW3cgiLgiqgYAcA0tPT8dxzzwWzLUREUUFZPqGt3QGHCKz96jBwNtjxZ2KtlAYw2gKc7igcnyX37JiMiRhXtlHOyrx02sio6M2i6OR3sFNTU4Ps7GxoNBrU1NR0um9OTk63G0ZEFKkKx2dhwSpnz45EmUTQnJPuMe+OkghnD9D2+hNosFhR/O5u9E3UwWK1obyqNubKMlRU12PJuv1ISohDUoJWHrqLxfdKkcfvYCcvLw9NTU1ITU1FXl4eBEGAp3yEgiBwRRYRxbTyqv+4VRZXlmzwFehIKmsaUXJTtjwkBjjn68RiL095Va08OVs5STsW3ytFHr8zKNfX12PQoEEQBAH19e7LDJUyMqIrSmcGZSLqCk+TjxdPyZaHaHwxGTvmpazZ1SgHTgW56Vi/twmtNgduzE2P+mEd138njQAkJ+pUPTuA899jS9EEAOphPfb4kC/+fn8HVC4i1jDYIaKuGFe2Qf6y1mmETgOcujKzvL9GAJ65KRuA56R6WgFyD49WAGpLo7dchLfVaNKk7LySD1U9PNL2cWUb0WCxwmTUY0vRNT3fUIpq/n5/+z2MtXr1ar9fvKCgwO99iYiizZaiCfKXua9AR9pf+hLvrGyEFOgIiM2EesrsyErOQqdOnS1nJwqU38HOlClTVPdd5+wIQsd4NefsEFGs89WjI32x55Wsg8XaDr1OA5NRj3OT4r0GO1rBGeRsrz+B0YNjJ42HNA9JOSx11UX95arqyuG6ri5nJ/KH3xmUHQ6H/PPhhx8iLy8Pa9euhcVigcViwfvvv48f//jH+OCDD3qyvUREEaGzQEcrALdfNvDsUI0zOZ7V5nArhyAx6nUwGfUouSlbXp3lKStxNMgrWacawtIKkIejlKUlttefgF103hL1tIDy7Dz88MN45ZVXVKUh8vPzcc455+DnP/859u3bF7QGEhFFGl/ZkfsmxrnNyTHq4zwGMFoBeDx/qKo3I5qHcaTgDujoqQLUpSVmjMngcBWFVEDBTm1tLYxGo9t2g8GAurq6bjaJqPeQhjh0GgEOUXTr0qfIUlFd73FiMeDsnQGcgYtyH6NeJwcz0kqjURn95IKgroFOtA/jGPVxsFjbYdTHYWdxvrzdNbiJ9vdJ0SWg1VhXXXUVEhMT8be//Q0DBgwAABw5cgR33XUXWltbsWnTpqA3tCdxNRaFg6fegWhfgRMMkbj0eO6KHV5z57iWfACAgmWbUdPQ7Fb/iYiCK+irsZT++te/4uabb8agQYMwcOBAAMChQ4cwZMgQrFq1KqAGE1FsrsDxlxQgSBN/IymzblcCHQAMcIgiTEDBzoUXXoiamhqsX78eX3/9NQBg2LBhmDhxompVFhH5R5rb0JuHsKSJu866ScHJIiwNE0o8BSdzV+yQVwUpE/z5UpAb2YGpsjcqx2TAsZYzqt4yKbjUCEBCnAZWm0P1fE8rqIiiVbeTCra2tiIhISGqgxwOYxGFXzCHfiqq67Fg1W63wEUZ7Eiv11XKbL+RLGtepap2FwBVoj5fk6xd9yeKRP5+f/u99FzJ4XBg0aJFMJlM6NOnDw4cOAAAWLBgAf7yl78E1mIi6tVWz7kSdWXmoAwBLVm332MPjXLpcyCBTl2ZOSoCHUA9JJpjMrj1lkl5gDQCoNe5fxXEao0u6p0CCnYWL16M5cuX4/nnn0d8fLy8PTs7G//7v/8btMYREQVTg8WKktV7/OrViHbKpIT7Dp/ElqJrVENSUrDnEJ05gJR98zqtwCEsiikBBTuvv/46/vSnP2H69OnQarXy9tzcXHkODxFRKFVU18s9N4NSzvG4j1ZQJwPUCoBep/W4b7RT5vTxpzipcg+bXYzapIZEngQU7DQ0NODCCy902+5wOGCz2Tw8g4ioZy1Ztx8NFiuWrNvvdYjKLjqHbQDnME5tqRnzzcNC2MrQUQ5B6TS+51R66tkhihUBrcYaPnw4Pv30U2RkqLs43377bYwc2XtXkxBR+LW1u9fmqyszy1W2pU6OmoZmv4ezonH+imvSPtf8Rd6WzRPFooCCnYULF2LmzJloaGiAw+HAP//5T+zfvx+vv/463nvvvWC3kYjIp6QELSxWG9ra1UuopSXiLW3tnp7mRiMAylEfk1GvKnMQrVzLNRD1JgENY910001Ys2YNPvroIyQlJWHhwoXYt28f1qxZg2uvvTbYbSQi6lRFdT0aLK0AnIGKyajH4inZqCszY+m0kcgrWefXvJW6MjOeuSkb2rNjOlKPTjT27LiKlfdBFIgu9+y0t7fjueeew7333ov169f3RJuIiLrEdTJtg8WKt7YdknswlIkFvVFOa+mb6KxzJQ35RFNPiLL+1vb6E/J7KK/6DxosrXhq1W4sWLUbl7gkGozEMh1EwdLlnp24uDg8//zzaG/3r0uYiKinFY7PkntjJMpJyka97+s6h9gRKFisNpxqjc7FFtJwVWVNozxsBUDu+QKcK69qGppVjyuHuYhiTUDDWBMmTIi6Yp9EFLve2nbILVuwlDQP8K9nB4Dcs6EVnCu3ovGLXxquMuekq4atTMZEeR8B7okGOcxFsSygchGvvPIKSkpKMH36dIwaNQpJSUmqxwsKCoLWwFBguQii6KZcVaXXaXCm3QFzTrrXAp6uFk/JRnlVLY63tKlqRAnoyD8TLWUiJJ5Wmul1GuxbNBmAum6YXqdBSlKC29AXUaTr0arnDz74IADgd7/7ndtjgiDAbndf+klE1FNyTAZ52EoKVrwFOka9Dhar7ezvcdhZnA8AeGrVbrd9lVeCymGgaKUM5JS9XVabAw0WK5qarXKPFoMdiiUB18by9sNAh4hCbfWcK93m7HRGWq0lBToFyzb78ZxEn/tEOmUNLOU8Jr1O43HoiyhWBNSzQ0QUaTobthIA3Jibju31JwCIbqu1XDMu15WZMXfFDlTWNMKck46l06InWao0PKXTCHCIIkakO1ddSUNUFdX1mDEmQw70iHqDgHp2AGDDhg244YYbkJWVhaysLNxwww346KOPgtk2IiK/dTY/Rzz7eIPFKg9HKQMcqdfGZEyUMwuPHpyCNINeVVAzGkjDUzaHCLvYserKdXUWUW8SUM/Oyy+/jIceegi33norHnroIQBAdXU1rr/+erzwwguYNWtWUBtJRMHl2msRDb0Yc1fs8HvCsT90GkHu5eioDNUxFhatGYeN+rhOe3Y4REW9UUCrsS644AIUFRVh9uzZqu0vvfQSnnvuOTQ0NAStgaHA1VjU22TNq4RddFb9Vg7/aAWgtjQyayZJbfaH9IUvAFg0JVs1+VgjAOcbnCUgTEY9thRd4zGhHpPsEUW+Hl2NZbFYcN1117ltnzRpEp588slADklEIZRmSESDpRVphkRU1nT0lvgbTIRawbLNfrfNU4HLt7YdQk1DM3JMBqyec6UqkAHci2Z620ZE0SmgYKegoADvvPMOnnjiCdX2d999FzfccENQGkZEgeusV0KZX6XR0ooIjW9UXCcQe+OtkvfqOVeq7jOQIepdAgp2hg8fjmeffRZVVVUYO3YsAOecnS1btuCxxx7D0qVL5X3nzp0bnJYSkd86m2+izK+SqNOocq8QEcWigIKdv/zlL+jXrx/27t2LvXv3ytuNRiP+8pe/yPcFQWCwQxQGheOzVMM0StJ8FgAYktpX1WtSkJsesjYSEYVKQBOUYw0nKFNvolzVpBU65u9I9yNlgrKncged8TaERUSxy9/v74Dz7PgjOTkZ3377bU++BBG5qKiux7iyjaiorgfgnKOTWVSJvJJ1AKCakGzOSVeVQTDnREbPDgMdIgqmHg122GlEFHrK+TpAxxwd6dackw6t4ByyWjptpCqhXqTm2JHodRq3wIaBDhH5wnIRRDFHVN3qNAJsDhE6jTNh3tJpI1VBTaRV8u6sV0eq2B2uAIe5d4iiU4/27BBFgrkrdiBrXiXmrtgR7qaEhDQsJd06zvawOqKgp7Wrw1eh5tprRkTRgcEOxbzKmkbYRfVclViWYzKobqVhq0iZj+NNZ4GORgjvcJU0D2pURj9WBSeKQj06jCUIgu+diHqYOSddrvvUG7gm0HMdtooEFdX1KFm9BzaH996mSJqLI/XoAMCWomvC3BqKdhwODb2wTlAuLS3FZZddhr59+yI1NRVTpkzB/v37Vfu0trZi1qxZOPfcc9GnTx9MnToVR44cUe1z8OBBmM1mnHPOOUhNTcUTTzyB9vZ2EAHOL/vaUnPEfeH3ZuVVtVET6ADOvEXs0aFg4XBo6AUU7DzzzDP44Ycf3LZbrVY888wz8v21a9fCZDJ5Pc6mTZswa9YsVFdXY/369bDZbJg0aRJaWlrkfR555BGsWbMGK1euxKZNm9DY2IhbbrlFftxut8NsNuPMmTP47LPP8Nprr2H58uVYuHBhIG+NiEKgs6Ah0gIdwFleYkvRNbwKp6Bg8Bx6ASUV1Gq1OHz4MFJTU1Xbjx07htTUVNjt9oAa89133yE1NRWbNm3CVVddhebmZvTv3x9vvPEGbr31VgDA119/jWHDhmHr1q0YM2YM1q5dixtuuAGNjY0YMGAAAOCVV17Bk08+ie+++w7x8fE+X5dJBYl6jtRlLw0D+aIMdjrr7udQABH1aFJBURQ9zsfZtWsXUlJSAjkkAKC52Zm2XjrG9u3bYbPZMHHiRHmfiy++GIMGDcLWrVsBAFu3bsUll1wiBzoAkJ+fj5MnT2LPnj0eX6etrQ0nT55U/RBRz+hKoOPtuZ66+zkUQET+6lKw069fP6SkpEAQBFx00UVISUmRfwwGA6699lrcfvvtATXE4XDg4Ycfxrhx45CdnQ0AaGpqQnx8PIxGo2rfAQMGoKmpSd5HGehIj0uPeVJaWgqDwSD/DBw4MKA2E5FvvgKdujIz9DotAMi3ks66+zkUQET+6tJqrBdffBGiKOLee+9FSUkJDAaD/Fh8fDwyMzPlKuhdNWvWLOzevRubN28O6PldMW/ePDz66KPy/ZMnTzLgIQqj+eZhHguXzhiT4XWIqrPHiIiUuhTszJw5EwAwePBgjBs3DnFxwVm5Pnv2bLz33nv45JNPcMEFF8jb09LScObMGVgsFlXvzpEjR5CWlibv88UXX6iOJ63WkvZxlZCQgISEhKC0nSgWhXo+DAMXIupJAc3Z6du3L/bt2yfff/fddzFlyhT86le/wpkzZ/w+jiiKmD17Nt555x1s3LgRgwcPVj0+atQo6HQ6bNiwQd62f/9+HDx4UO5BGjt2LL766iscPXpU3mf9+vVITk7G8OHDA3l7RBHNtdBnT+jqfJiK6npkFlV6/OnM4inZwWguEVGnAlqNddlll6GoqAhTp07Ft99+i+HDh+OWW27Btm3bYDab8eKLL/p1nAcffBBvvPEG3n33XQwdOlTebjAYoNfrAQCFhYV4//33sXz5ciQnJ2POnDkAgM8++wyAc+l5Xl4e0tPT8fzzz6OpqQk/+9nPcP/99+O5557zqx1cjUWRbu6KHXJixO31J+R5MFIxT2VQEYyl213t2QmkzEMkLjEnouji7/d3QMGOwWDAv/71L2RlZeHXv/41Nm7ciHXr1mHLli346U9/ikOHDvl1HG8Zll999VXcfffdAJxJBR977DGsWLECbW1tyM/Px8svv6waoqqvr0dhYSGqqqqQlJSEmTNnoqyszO9hNgY7FGmUwc3SaSMxuKgSIgABQLoxUa57pRUAu8tfcKiDCH8DHQY3RBRs/n5/BzTpRhRFOBwOAMBHH32EG264AQAwcOBAfP/99106ji+JiYl46aWX8NJLL3ndJyMjA++//77fr0sU6ZT1vJZOG4lEnQZWmwOJOo0c6ADOUhird/WOml9ERIEKaM7OpZdeisWLF+Nvf/sbNm3aBLPZecV24MABt2XgRNR1rsU755uHw2TUY755uFzgEwA++fdR1fOM+h4td0dEFJUCOjO++OKLmD59OlatWoX58+fjwgsvBAC8/fbbuOKKK4LaQCJSr1Yqr/qPvN1ibYdRHweLtV2+T0REagHN2fGmtbUVWq0WOp0uWIcMCc7ZoUhSsGwzahqc2cS1AtA3MU4OanYW56vmyCgDHen+zuL8Hm1fVycjL56SzWXlRNQjerRcBABYLBb87//+L+bNm4fjx48DAPbu3ataAk5EXScFOoBzGMu118ZkTJRvdxbny0NXoQh0fHGdhFxXZmagQ0RhF9AwVk1NDSZMmACj0Yi6ujo88MADSElJwT//+U8cPHgQr7/+erDbSdRrmM6utjIZE7F02kis/eowbA4ROo1z9eKWogmq/cMd4EikQIerrogo0gQU7Dz66KO455578Pzzz6Nv377y9uuvvx533nln0BpH1DsJqlvH2ZFmR/BGnP1SUV2PJev2w2K1hfR1iYiCLaBhrG3btuEXv/iF23aTyeS1+CYR+ce1wKXryqxQKa+q9SvQ0QjszSGiyBZQz05CQgJOnjzptv3f//43+vfv3+1GEfVmrnWilk4biaXTRvb46yonRvuDAQ4RRYuAenYKCgrwzDPPwGZzXvUJgoCDBw/iySefxNSpU4PaQCIKja4EOkRE0SSgYOe3v/0tTp8+jdTUVFitVvzkJz/BhRdeiL59++LZZ58NdhuJKMLodQEv5CQiCrmAhrEMBgPWr1+PLVu2YNeuXTh9+jR+/OMfY+LEicFuHxFFiLoys1yz69rhab6fQEQUIQIKdl5//XXccccdGDduHMaNGydvP3PmDN58803cddddQWsgEUWO7fUnYBedt0RE0SKgDMparRaHDx9GamqqavuxY8eQmpoKu90etAaGAjMoU6yrqK5HeVUtCsdn4YsDx1XFQ/Vni4z6UldmVh0n1MkC80rWwWJth16nQUpSAhosVvkxZmkm6p38/f4OKNjRaDQ4cuSI28qrXbt24eqrr5YzKkcLBjsUa6ThpjRDIpqaW2HvRoqeSFh1NbioEp29BaNeh53Fk0LWHiKKDP5+f3dpGGvkyJEQBAGCIGDChAmIi+t4ut1ux4EDB3DdddcF3moiCorKmkbYRaDB0trl54YjuPHVY6QMdPztiSIiknQp2JkyZQoAYOfOncjPz0efPn3kx+Lj45GZmcml50QRwJyTLgc80aC8qhYNFivKq2o9BjsCOgKelKR4VRCn0wp4PH9oaBpKRFGpS8FOcXExACAzMxN33HEHEhMTO91/xYoVKCgoQFJSUuAtJKIukxIRjivbgAZLK3QaATZH5EY+heOz5J4dTwyK6u4NllZV744tWiI6IgqbgJJlzJw502egAwC/+MUvcOTIkUBegoiCYFRGCrQCIjrQAZxZo7cUXeN1krEU6ADOQqmuw1jlVbU92j4iim4BLT33VwBzn4koCDKLKgN6XiRMRvbEeLZnx6iPw5aiCRi2YC2sNgc0gnOIa1RGv3A3kYgiWI8GO0QUWv4GOXVlZrd9tYKXncNAWmaupFxevm/RZADAuLKNaLBYmfeHiDrFnO9EMcLfQEcKalyDm1BXVe+Ma6ADeB6qcq0QT0TkCXt2iHoZKagx56Rjza5GJOo0mG8eHlFJ+YyKCckSTwGNa4V4IiJPGOwQ9TJLp42Ub6XfI83O4nwAQMGyzahpaEaOycCghogCFvBqrE8++cTnfhkZGdDpdIG8BBF1kSaC5twES01Ds+qWiCgQAfXsNDc3Y+LEicjIyMA999yDmTNnwmQyue23e/fubjeQiNQ9HN354u+J1VZzV+xQ1dpyJc2pCaRnRnq/OSZDd5pIRL1cQD07q1atQkNDAwoLC/GPf/wDmZmZmDx5Mt5++23YbLZgt5Go16iorse4so2oqK5Xbe9KD4dGcA9q6srMPbasvLLGe6ADQM6MHIjVc65EXZkZq+dcGdDziYiAbqzG6t+/Px599FHs2rULn3/+OS688EL87Gc/Q3p6Oh555BF88803wWwnUa+wYNVuNFisWLBK3SvalZ6Nb0udQY0U4PR07hxfq7i4WoqIwq3bS88PHz6M9evXY/369dBqtbj++uvx1VdfYfjw4XjhhReC0UaiXkNU3Cp7eCK5Z2N7/XH5d72u45SiEbo3hEVEFCwBBTs2mw3/93//hxtuuAEZGRlYuXIlHn74YTQ2NuK1117DRx99hLfeegvPPPNMsNtLFNOMeuc0OgHdG/4JJWVRTmUZB4cYPe+BiGJbQBOUzz//fDgcDkybNg1ffPEF8vLy3Pa5+uqrYTQau9k8ot7l8fyLUV5Vi1EZ/bC9/kTEDf9UVNfLBTul3hqTMVEOeJQFOjUCcL6BQ1hEFH6CGEABq7/97W+47bbb/CoGGg1OnjwJg8GA5uZmJCcnh7s51ItJ5Q8CEYq6Vt7aZzLq5QANENFgaUWOyRDRw29EFP38/f4OaBjrZz/7WcwEOkSRJNBAZ/GU7CC3xDNvvTQNFisqaxrRYLHKvTzMjUNEkYK1sYgihL+1rYx6HUxGPaQcgloBIZsAPGNMBgpy093qapmMephz0mEy6mEyOi+EmBuHiCIFgx2iKPN4/lBsKboGN54NOkJdwHPptJGoLTXLwUyOyYAtRddg6bSR2FJ0DQrHXwiTUY/bLxsY0nYREXnDYIcoykTKMm5viQ7Lq2q5CouIIgqDHaIoVVnTCLvoO4NxT1H27CgVjs9iIkEiiiisek4URj+aVwmHn+shXVdbmXPSUVnTGPJhLIm3lVYzxmRETO9TsCnrgHG1GVH0YM8OUZjMXbHDa6BTV2aWq5h7qnUFdMydWTptZA+2kpTWKAqecrUZUfRgzw71ehXV9Xi2ci9abQ7cmJuOpdNGekye54mvit+BkAIbqcZVbzN3xQ65xyrSArkuJyUj6iZ/z0XhaEsk/626Ys8O9XrlVbWw2hwQ0TH/Zcm6/WiwWLFk3X6vz6uorg96oMPl2uGfi+QvnUbwvRNRN0XShH/XtkTL3yrAYIcIheOzoNdpIADQCAIyiyphsdoAAG3tDq/Pc61M3h1SdXLOAXHORQrHknp/CIrbb567PpxNoV6iuxP+K6rrVUWFlfddH5u7Ygey5lVi7oodHu9LSU+lW/vZrk57FHR5BlQuItawXERs6kr3r9Qd6/pHq9MIXr/U/E0C6EsoyjwQUe8klXgxGfXYUnQN8ko+hMVqg1GvQ8uZdtjsInRaAd88ez2y5lXCLjoTlZbclI2nzl7QaQXnhaCtk9UU4TqP9Wi5CKJo0JXuX0+BDoBO/7iJiCJdZz1DtrMnPelW2auqPG+ac9JV58JoHMBlsEMxqyvdv9IfeY7JAJNRL8/H0Ou8/4lE4x88EUWPiup65JV8iLySDz0OQ3l7jvLxGWMysKXoGrl3+/H8oTAZ9WdvnaVdpNvRg1OQZtBj9OAUFI7PglGvg1Gvw+jBKao5ape4zC3s7DwZKSK/hUQBcv0j78x7Z3t2ahqa0WCxylcxVpv3OTuuf/BERMFUXlULi9UGi9Um97T46rH29bjyvNjSZgcA+Vb53Le2HVK9dmpygnyMmoZmOUACOj9PRgoGO9TrDS7yP7GfEvOsEFFPUvauSD3UvnqsuzOhWflc5fmtcHwWGiyt8v0ck0F1Xxn4RCoGO9RrVVTXI7OoMuDcKUY901QRUc9x9r60w2K1oWT1HgDAwnd3o8FixcJ3Pa8G7UqPtnJICwCeWuU89lMuK02fWrVbPt8JAG6/bKAqwGlpaw/k7YUUgx3qtZ6t3Odzn85SqViskf8HTkTRTRpStzlE5JWsk3uhvfVG+5rT4420vNw758lQhHO4a1RGivxINJwLGexQr9Vqs/vch4uxiChS+BNUdGUVqnJffxMDCnAOayn3j4bFGgx2qNe6Mdd30rpoWGVARLGrINe5UrQgN111PvI2jN6VOTvKfUekdyy4cJ2DI6BjyGvRlGzMGJMh76/TClg0JTuAdxZanHRAvYqUaBAQVRPsAOekO+WkvILcdKze1Ri05IGeFPgRcBFR77W9/gTsovM2JSlBzl7srZdnxpiMgGpoKc99DZZWaISOnm1PHdwHj/8AAEiKjwt7zS5/MNihXqNg2WavK6j0Og1Wz7lS3icUNaqYOZmIfCkcnyVnggecE5QdYmAroJRZ5ZWTkF2HvEzGRNXFoEZQD3lFQ3Djin301Gt0FujMNw8HAKyec6Vco6orxe0Wu3TjFuSmy93MRn0c6srMche0XqdhoENEflGurpoxJgPfljrr6G0pmtDlY3mbzyPVBwSc56ctRRPkJII6jYBvS81uw2OuK7kiHWtjgbWxegtvPTveAo+5K3b4VdWcgQsRRQKpxp85Jx1Lp410e3xc2QY0WFrdem6i+RzG2lhE3eRPoENEFCjXquKBLhuXrN7lzAQvnbuGLViLzKJKDFuwFgDkAKfB0qrquekNGOxQr8GMx0QUSdacDU7WnA1Onq3ciwaLFc9W7g3K8aUyDtKtFNdoBHX+nt6AE5Qp6ikn3XU2cc51tRXgHJ8OxmqrYQvWwmpzQK/T4NrhaapeoYLcdKzZ1YhEnRbzzcMwY0yG320motiVqNPAanMg8ex8mVZFcDJ3xQ4snTayS+cKnUaAzSF67a1JTtTBYrUhOVGHQSnnhGwxRiRgzw5FPX+TaK2ecyUWT8mGUa+DXqeBUa+TTy6BkoIl5RWU6/BXZU0jRABWmx3lVbWYu2KHnJbdn8RfRBSb5puHw2TUywsklLm/pAUSyvObr2Gu4oIRMBn1KC4YAQCqRRKAelKxcjFGb8AJyuAE5WgXaC+Jvz063ibvSZP9fJF6djz9oS0+m6CLiHon1/OXt0nEngRzYrFyAUeOyYB9h0+qhrhcX8t1/3AFTZygTL1GVwrfBZM/gY5OI2DptJE44OGklGMyMNAh6uVce6abmltVt6GiHOKvaWj2OZfHdf9Ix2CHKED+JPVSnjBc9z/WcibobSKi6OKav8ac4ywPYc4JbXZ15dydHJPB5yot1/0jHYMdogApq/56ozxhbCmaIM8ZMup1ftWuIaLY5tozXfd9C+yi83bxlGy5yKZRH6c6n3gLRQJdvn77ZQPl1VrHWtpQXDACep0GApxD8a7L5KU5P9Ey74ersahX6erKq4LcdGTNq4T9bAeNRgCeuSkbS9bth8Vq8/lc18Re5VW1sFhtMBn1HMIi6uVc5+tUVNfLQ0I1Dc041nJGnuvnWgvL2yBToGUdyqtq5VpYDZZWlFfVygsvpMnSdtH5u6eEhZEurD07n3zyCW688Uakp6dDEASsWrVK9bgoili4cCHOP/986PV6TJw4Ed98841qn+PHj2P69OlITk6G0WjEfffdh9OnT4fwXVC06Gqgs3hKtpykS+IQOwIWpRyTQdWVa9THeTwhdKUiMRHFtgVnV2UuOFunSlmvKsdkQOH4rC717FRU16OlrV3uOS5YthmZRZUoWLbZZ1sKx2fJPTsmY6JcQkKAc0gtXMNrwRLWnp2Wlhbk5ubi3nvvxS233OL2+PPPP4+lS5fitddew+DBg7FgwQLk5+dj7969SEx0zn+YPn06Dh8+jPXr18Nms+Gee+7Bz3/+c7zxxhuhfjsUpbytaPjRPM/BUeH4LCxZtx+AcylnV66eAq1ITESxR3S5VZKGhrpyvpB6nI16HWaMyZCDJ38mEHs6N7nej8YeHUlYg53Jkydj8uTJHh8TRREvvvginnrqKdx0000AgNdffx0DBgzAqlWr8NOf/hT79u3DBx98gG3btuHSSy8FACxbtgzXX389lixZgvT06IxAKfgCSRzobTECA5aew2SL1JsIcAY6vaNgQ3hF7ATlAwcOoKmpCRMnTpS3GQwGXH755di6dSsAYOvWrTAajXKgAwATJ06ERqPB559/7vXYbW1tOHnypOqHYlegGZI9rbbyZwUWBc61W58oli2akg2TUY9FU7IBoNv1qlwrkRfkOoeeCnJ54R+xE5SbmpoAAAMGDFBtHzBggPxYU1MTUlNTVY/HxcUhJSVF3seT0tJSlJSUBLnF1F1zV+xQJd/T6zRISUqQ57c8W7lXnjDXHf4m4tpSNKHbr0Vd01m3PlGsce0l/ua564N6vKXTRkb10FMwRWzPTk+aN28empub5Z9Dhw6Fu0kEuGUZttoc8qoC5cqA7ghmxlEKPtf09kS9XXcroZNTxJ5R0tLSAABHjhzB+eefL28/cuQI8vLy5H2OHj2qel57ezuOHz8uP9+ThIQEJCQkBL/R1C2JOi2sNrtqm3LlUnd7dhjoRL6dxfnhbgJRRAl0KTmpRWzPzuDBg5GWloYNGzbI206ePInPP/8cY8eOBQCMHTsWFosF27dvl/fZuHEjHA4HLr/88pC3OVRckztFm4rqeuSVfIi8kg9VVyspSTqvz5kxJgP7Fk2GUe99HyKiWMN0FcER1p6d06dP4z//+Y98/8CBA9i5cydSUlIwaNAgPPzww1i8eDGGDBkiLz1PT0/HlClTAADDhg3DddddhwceeACvvPIKbDYbZs+ejZ/+9KcxvRKrsqYxqpM7KfPUKK9WPNWaarBY8dSq3fjiwHGMHuzMWGzU6+Ql367zfIiIYglXfwZHWKueV1VV4eqrr3bbPnPmTCxfvhyiKKK4uBh/+tOfYLFYcOWVV+Lll1/GRRddJO97/PhxzJ49G2vWrIFGo8HUqVOxdOlS9OnTx+92RFvVc2VV3GicRFtRXY8Fq3ZDRMfcDNfsoD2Bw1hERLHF3+/vsPbsjB8/Hp3FWoIg4JlnnsEzzzzjdZ+UlJRel0AwXFVxg8lbCnStAFXGYiIiou6K2Dk75F20p+0ur6r1uN2oj0NtqTkqKugSEVH0iNjVWORdtOdOODcpHg0WK3JMBmSel4TKmkaYc9IxenAKxpVtxKiMfn6lN/fEZEz0OPeHiIh6LwY7ESjWU+bvaWyWb1fPuVIO3MaVbUSDxYqmZmvAx2agQ0RErhjsRKBYz6swIt2AmoZm2EVg2IK1qtw5WqHjcSIiomDgnJ0IocySGet5FZSBjGuSQLsIHGs5E5TXKchNx+KztWcWn609Q0REvQ+DnQjh2puzpeiamOzV8SWYQd7SaSNV/65ERNQ7MdiJELHem6PUWUHfYAV5UnXy3vTvSkREnoU1qWCkiLakgtEua16lnEtHQM9VuDYZ9QBENFhakWMyYPWcK3volYiIKBz8/f5mzw6FXKjyAzVYrPLqLE54JiIKvUip2s5gh0Ju9OAU6HUaCADSzw43dZfJqIen0TG9zvlfnIkKiYhCL1LmTTLYoZBbsGo3rDYHRPjOiyMFK9KtRLnKyplI0OpxOOxMuwN1ZWYOYRERhUGkzJvknB1wzk6oZRZVdvsYUg0tvU7jtnwdAHRaAQ6HCHNOelRnmyYiIu+iohAo9T7BCHQAZ6CjFYBWRaBjMiaicPyFMZ19mogoksxdsUNV8idSz78MdihqjUhX19aSenAi7Y+MiChWVdY0wi46b7fXn4jY7P8MdiiimIx6jMroh+31J9DoZR6OxLW2FhERhZY5J12+4ASApmYrRmX0C3Or3DHYoR43d8UOrN7V6HM/kzERW4quUT1P+iOq+74FNQ3NbpXSiYgofJZOG6kq5mwXge31J8LcKnecoAxOUO4peSXrYLG2+7VvXZm5h1tDREQ9qaK6PuRzdjhBmcKus0CHwQ0RUWyZMSYj4ubqSBjs9AIV1fV4tnIvWm0OXGIy4Jujp2G12QE461R9W+o98KiorsdTq3YHrS0McoiIKNQY7PQC5VW1ci4a17IJDh+DmMHMemnU64J2LCIiIn8x2IlxFdX1aGlrh0bwHNh0VoF8XNkGnxmO/RUJGTSJKHZ0dX5IT88nkeYoGvVx2Fmcr7p/1UWpqkUa7OEOPZaLiGEV1fUofnc3LFYbPE1D1+u0eOambK/PD1agU1dmxpaiayJ2LJeIok9nNZdci09WVNdjwardaLBYsWTdfo/H8/Qc5f1xZRuQWVSJcWUbPD4mzVG0WNuRWVSpuu/PalTqWQx2Ylh5Va2caThR5/5RW232sBdnIyIKRGc1l1wDofKq2k5zdnl7jvK+dPHXYGn1+hgAGPXqARPX+xQeDHZimHQyKLkpG/PNw6E7O2al0wgoyE3n0BIRRa0ZYzK89hgXjs+CUa9DS1s7Kqrr5ftGvQ6P5w/1eDzX4GlURj9oBcgJ8kzGRPnW9fjSYxoBeDz/YtVxW9rsqvuuRY0pNJhnB70jz46yJpVRr4PFapPvexs/DlYdK45PE1GojSvbiAaLFSajXpWsNFjPzyv5EBarDUa9DjuLJ2HYgrWw2hzQ6zRot4uwdbL6g+fE4PH3+5shZi8wbMFa1X1loENEFIs6G+bqiedLK16tNocq0NG5rAJhz0548F89xo0r2yD/EXZVJwu1iIgilnpC8tcAgCG/eh+ZRZUY8qv35X06m5Dc2TAZ0HHR6OvicfIl56vuJ8Txazcc+K8ewwqWbfa5oqqzyXO9fnyTiKLSknX75fOXxdqOcWUb5N4W6fbZyn1osFjxbOU+AEDJ6j1osFhRsnqPz+O7DvG73i/I7ajbV1nTqOrN8beEDgUXg50Y5ppA0BP+4RFRLDPq41QXfdKwUuvZLPLSrWsw1B3KQpjmnHRcOzxN1R4KPQY7McyfsWFpFQERUax4PH+oHNQMSklSnQulYaUbc9OhFZy3AJBjMqhu/eV6DtVpBHklV0FuOpZOG6kKfgalJHX9DVG3cTUWYm81lpQptMFi7XQ/AT0/VGUyJmJL0YQefhUiIjXl0JJWAOxix++1ndQD7OqxAWeGeOX5VrovreRyrTHI1VjBw9VYvdDcFTuQNa9SHntWyjEZVN2nGi+JBrvCtefIdUKzM3MyAx0iCi7XycSeKHtqzDkdc2jMOeluz5fOnXNX7ADgnO+YWVSJgmWbATiDG+kHUA9FSXl3JDqN4LaSa8aYjIB7jig42LOD2OnZyZpXKV+9SDrLMeFvHh1ehRBRJHHNcdNVrjl0pHOn1OujPDfWlbnfp8jBnp1eSHn1Ijl6Mjj1rYiIYoVrz4s5xzl/RzqHshcm9jDYiSF137e4bQvGygIiokgyKOUc1a3rMJQvrjl03qtphF103nrC4Cf6MdiJIZ6Wmrtm7yQiinZ7GptVt5Vng5VKL8GKL9I1oXQrnUul29svGwiTUY/bLxvYjVZTOHHBfwzRaQXYXCftoPs1rqSaL4BzErLBQ22tgmWbUdPQDJMxEcdbzsj7czUWEQWbOScdlTWN8rCT631X0grVwvFZXjMiK+WYDKhpaJZ7cpRVzv15PkUe9uzEkOIbR8Co10Gv00Cv08Co1wVlGEtZbkKE5/To0hVQg6VVtb+vDM5ERF21dNpI1JaasXTaSPl+yU3Z2F5/wuMKLWWw4onrMNXqOVeirsyM1XOuBND9OlsUflyNhdhZjeWqu6utpN4aX4x6HZIStB4DG/bsEFEoDPnV+7A5ROg0ApIStJ1mh/d0zpPOdzkmgxzkeDN3xQ6s3uUcMivITUfd9y3yuVKv02C+eTierdyHVpsdN+amY/TgFDxbuRetNgduPJtokIKDq7FigD+5JHqSP4EOIPX0CNAqpgcJcAZBheMv7JG2EREpKcs9BFIGx3WeTmeUc4MqaxpVz7HaHCivqoXVZod49nHnfYd8n0KPwU4E89X12tP8XXngDGqyYM5JhwDnlU2iTguL1YYl6/b3bCOJiKAeigqk/lRXVly5JilUPkev06BwfBb0Oi2Es48772vk+xR6nKAcwQrHZ8mT6sLh9ssG+nWV83j+UMwYk4EZYzLk7tm8kg9hPVtgj4iopymHnob86n35d51GUM1d9LY+9fbLBuJYyxm/VlyNHpyC9Xub0Hp2fmLmeUnyuTIhTiOfD5U4sTm8GOxEME9/MJ1RjiN3xeCiSgiCc9mlTiMgNTlRDrQ6I63+8rRC4fH8oWEN1Iio93BdbaUMblwXaXibpNqVFVfSsBTgPiwVyBAa9TwOY8WQrgY6JqMegPOPXzof2BwiGixWFL+7G6My+sn76jSCW1dt8Y0jvK5QcE3aRUQULK61q55atRsNFqtcbFOZX8yoj1PV8RM8PL9g2WY0WKzQaQW/LtBch6WUQ1OBDKFRz+On0gt4W21VUV2PZyv3qYabdBoBDlGEXQS215/wWQeGwQwRhZqvycTfPHd9p8+XVqq6HsdmF/06p3nqdecKq8jGYCfMlN2vXxw4jjW7GhHnMsbcU6Q/WOUS9W+eu17VJiKiWCPN45F6gKQheZ3W24we6o4fzauEQwQ0AvBtaXgKqTLYCTNpnLj43d1wiM4hpVDXs3LNFtrVuUKRZu6KHXI2VV5tEcUekzERDZZWmIyJANyDF1+KC0aoLuiKbxzBC7wgky6aR2X0cyvHEQ5MKojwJhWsqK5H8bu7YRcBvU6L1rO5GbrD19BTrBtcVCn/G0pziqI5eCMiijbjyjaiwWKFVgCUVYyC/f3EpIJRpG+iDka9DtcOHwCDXge9ThvuJkUlKQljnOLqLpx5ioiIehupAv25SfEA1IFOOHEYK8zKq2phsdpgMurxyb+/k+tOBauXp7dwXXav0wiYfMn52F5/gl3TREQhIlWglyrSRwr27ISZtwJzCXEaLJqSDZNR73cm497Mddm9zSFi6bSRXP5ORBRC5px0aIXIyxTNnp0wqqiuR8maPbDZRTk/hMRitcnbGizWcDSPiIioS5ZOG4n3ahrlC1ABHYkcw5mDiMFOGLkGOOSZa3ZUibJKcUFuOiprGqERnKsymNiLiCg0lNMI9DqNatXVgTKzvGikOYzZpfmNEIXqyszyTPfewFsad2VCsNVzruQycyKiMFCWzJDKaAAddcgSdRpYbQ4k6sI3c4ZzdsJI340PvnB8Fox6XRBbE7m8zWvqSpViIiLqGd7m56SfLUk03zwcJqMe883DQ9ksFfbshFFbu8P3Th5kFlWqxkFjnbckh12pUkxERD1j6bSRWPvVYTmxo5QYVxp9iIREtezZCaPuZJNUPtVk1Mu5eXpLbw+gHt4iIqLwGFe2QQ5wQl0BwF8MdqKcUR+HLUXXYL55GExGPR7PHxruJgWFlCCworoeeSXrkFlUibySdap9vA1vERFR6DRYWsPdBJ84jBWFtAIwIt2AYy1n5C/6SOgmDCZlr43l7Ax+i8tM/lh7z0RE0Ug5dBUpS81dsWcnCknZKWN5COd4S5t8K03k7s6EbiIi6hnKoSvlIJbrBWo4RU7YFeOUuWK6m1/HZNRjVEa/mC6FIC1ftNocMBn1aLBYkZKUEOZWERGF37AFa1VLvAHnqtTVc64MS3tyTAbUNDRDpxEw7PxkOS2IVJU+EjDYCRFpWMafQMeoj4PF2i53AUq/7yzO7+lmRgzlv0Hh+Cw5UCQi6u1cAx2gI+9YOIQryOqKmAl2XnrpJfzmN79BU1MTcnNzsWzZMowePTrczZKz/HYW4Qa75H0scA3sODeHiMhJfzZJnxLzjXUuJoKdf/zjH3j00Ufxyiuv4PLLL8eLL76I/Px87N+/H6mpqWFtmxRte5qtXpCbzqy/RES9hLLETXd6Q/YtmhzEVvUOMTHj83e/+x0eeOAB3HPPPRg+fDheeeUVnHPOOfjrX/8a7qZ1Splim4iIYpuyxA2FVtQHO2fOnMH27dsxceJEeZtGo8HEiROxdevWMLbMmenYG63gPcU2ERHFHpa4CZ+oH8b6/vvvYbfbMWDAANX2AQMG4Ouvv/b4nLa2NrS1tcn3T5482aNtlEgz1sM5a56IiMKD5/3wifpgJxClpaUoKSkJ6WtyEjIREVF4RH2wc95550Gr1eLIkSOq7UeOHEFaWprH58ybNw+PPvqofP/kyZMYODD4xSQZ4BAREYVf1M/ZiY+Px6hRo7BhwwZ5m8PhwIYNGzB27FiPz0lISEBycrLqh4iIiGJT1PfsAMCjjz6KmTNn4tJLL8Xo0aPx4osvoqWlBffcc0+4m0ZERERhFhPBzh133IHvvvsOCxcuRFNTE/Ly8vDBBx+4TVomIiKi3kcQRVH0vVtsO3nyJAwGA5qbmzmkRUREFCX8/f6O+jk7RERERJ1hsENEREQxjcEOERERxTQGO0RERBTTGOwQERFRTGOwQ0RERDGNwQ4RERHFNAY7REREFNMY7BAREVFMi4lyEd0lJZE+efJkmFtCRERE/pK+t30Vg2CwA+DUqVMAgIEDB4a5JURERNRVp06dgsFg8Po4a2MBcDgcaGxsRN++fSEIQlCOefLkSQwcOBCHDh1iva0w42cRWfh5RA5+FpGDn0VgRFHEqVOnkJ6eDo3G+8wc9uwA0Gg0uOCCC3rk2MnJyfyPGyH4WUQWfh6Rg59F5OBn0XWd9ehIOEGZiIiIYhqDHSIiIoppDHZ6SEJCAoqLi5GQkBDupvR6/CwiCz+PyMHPInLws+hZnKBMREREMY09O0RERBTTGOwQERFRTGOwQ0RERDGNwQ4RERHFNAY7PeSll15CZmYmEhMTcfnll+OLL74Id5NizieffIIbb7wR6enpEAQBq1atUj0uiiIWLlyI888/H3q9HhMnTsQ333yj2uf48eOYPn06kpOTYTQacd999+H06dMhfBfRr7S0FJdddhn69u2L1NRUTJkyBfv371ft09railmzZuHcc89Fnz59MHXqVBw5ckS1z8GDB2E2m3HOOecgNTUVTzzxBNrb20P5VmJCeXk5cnJy5OR0Y8eOxdq1a+XH+VmER1lZGQRBwMMPPyxv42cROgx2esA//vEPPProoyguLsa//vUv5ObmIj8/H0ePHg1302JKS0sLcnNz8dJLL3l8/Pnnn8fSpUvxyiuv4PPPP0dSUhLy8/PR2toq7zN9+nTs2bMH69evx3vvvYdPPvkEP//5z0P1FmLCpk2bMGvWLFRXV2P9+vWw2WyYNGkSWlpa5H0eeeQRrFmzBitXrsSmTZvQ2NiIW265RX7cbrfDbDbjzJkz+Oyzz/Daa69h+fLlWLhwYTjeUlS74IILUFZWhu3bt+PLL7/ENddcg5tuugl79uwBwM8iHLZt24Y//vGPyMnJUW3nZxFCIgXd6NGjxVmzZsn37Xa7mJ6eLpaWloaxVbENgPjOO+/I9x0Oh5iWlib+5je/kbdZLBYxISFBXLFihSiKorh3714RgLht2zZ5n7Vr14qCIIgNDQ0ha3usOXr0qAhA3LRpkyiKzn93nU4nrly5Ut5n3759IgBx69atoiiK4vvvvy9qNBqxqalJ3qe8vFxMTk4W29raQvsGYlC/fv3E//3f/+VnEQanTp0ShwwZIq5fv178yU9+Ij700EOiKPLvItTYsxNkZ86cwfbt2zFx4kR5m0ajwcSJE7F169Ywtqx3OXDgAJqamlSfg8FgwOWXXy5/Dlu3boXRaMSll14q7zNx4kRoNBp8/vnnIW9zrGhubgYApKSkAAC2b98Om82m+iwuvvhiDBo0SPVZXHLJJRgwYIC8T35+Pk6ePCn3SFDX2e12vPnmm2hpacHYsWP5WYTBrFmzYDabVf/mAP8uQo2FQIPs+++/h91uV/3nBIABAwbg66+/DlOrep+mpiYA8Pg5SI81NTUhNTVV9XhcXBxSUlLkfahrHA4HHn74YYwbNw7Z2dkAnP/O8fHxMBqNqn1dPwtPn5X0GHXNV199hbFjx6K1tRV9+vTBO++8g+HDh2Pnzp38LELozTffxL/+9S9s27bN7TH+XYQWgx0iCppZs2Zh9+7d2Lx5c7ib0qsNHToUO3fuRHNzM95++23MnDkTmzZtCnezepVDhw7hoYcewvr165GYmBju5vR6HMYKsvPOOw9ardZtRv2RI0eQlpYWplb1PtK/dWefQ1pamtuk8fb2dhw/fpyfVQBmz56N9957Dx9//DEuuOACeXtaWhrOnDkDi8Wi2t/1s/D0WUmPUdfEx8fjwgsvxKhRo1BaWorc3Fz8/ve/52cRQtu3b8fRo0fx4x//GHFxcYiLi8OmTZuwdOlSxMXFYcCAAfwsQojBTpDFx8dj1KhR2LBhg7zN4XBgw4YNGDt2bBhb1rsMHjwYaWlpqs/h5MmT+Pzzz+XPYezYsbBYLNi+fbu8z8aNG+FwOHD55ZeHvM3RShRFzJ49G++88w42btyIwYMHqx4fNWoUdDqd6rPYv38/Dh48qPosvvrqK1XwuX79eiQnJ2P48OGheSMxzOFwoK2tjZ9FCE2YMAFfffUVdu7cKf9ceumlmD59uvw7P4sQCvcM6Vj05ptvigkJCeLy5cvFvXv3ij//+c9Fo9GomlFP3Xfq1Clxx44d4o4dO0QA4u9+9ztxx44dYn19vSiKolhWViYajUbx3XffFWtqasSbbrpJHDx4sGi1WuVjXHfddeLIkSPFzz//XNy8ebM4ZMgQcdq0aeF6S1GpsLBQNBgMYlVVlXj48GH554cffpD3+eUvfykOGjRI3Lhxo/jll1+KY8eOFceOHSs/3t7eLmZnZ4uTJk0Sd+7cKX7wwQdi//79xXnz5oXjLUW1oqIicdOmTeKBAwfEmpoasaioSBQEQfzwww9FUeRnEU7K1ViiyM8ilBjs9JBly5aJgwYNEuPj48XRo0eL1dXV4W5SzPn4449FAG4/M2fOFEXRufx8wYIF4oABA8SEhARxwoQJ4v79+1XHOHbsmDht2jSxT58+YnJysnjPPfeIp06dCsO7iV6ePgMA4quvvirvY7VaxQcffFDs16+feM4554g333yzePjwYdVx6urqxMmTJ4t6vV4877zzxMcee0y02WwhfjfR79577xUzMjLE+Ph4sX///uKECRPkQEcU+VmEk2uww88idARRFMXw9CkRERER9TzO2SEiIqKYxmCHiIiIYhqDHSIiIoppDHaIiIgopjHYISIiopjGYIeIiIhiGoMdIiIiimkMdoiIiCimMdghopjx9NNPIy8vL6jHXL58OYxGY1CPSUShxWCHiIiIYhqDHSKKKK+//jrOPfdctLW1qbZPmTIFP/vZz7w+b/ny5SgpKcGuXbsgCAIEQcDy5csBABaLBffffz/69++P5ORkXHPNNdi1a5f83F27duHqq69G3759kZycjFGjRuHLL79EVVUV7rnnHjQ3N8vHfPrpp3vibRNRD2KwQ0QR5bbbboPdbsfq1avlbUePHkVlZSXuvfder8+744478Nhjj2HEiBE4fPgwDh8+jDvuuEM+5tGjR7F27Vps374dP/7xjzFhwgQcP34cADB9+nRccMEF2LZtG7Zv346ioiLodDpcccUVePHFF5GcnCwf8/HHH+/ZfwAiCrq4cDeAiEhJr9fjzjvvxKuvvorbbrsNAFBRUYFBgwZh/PjxnT6vT58+iIuLQ1pamrx98+bN+OKLL3D06FEkJCQAAJYsWYJVq1bh7bffxs9//nMcPHgQTzzxBC6++GIAwJAhQ+TnGwwGCIKgOiYRRRf27BBRxHnggQfw4YcfoqGhAYBziOruu++GIAhdPtauXbtw+vRpnHvuuejTp4/8c+DAAdTW1gIAHn30Udx///2YOHEiysrK5O1EFBvYs0NEEWfkyJHIzc3F66+/jkmTJmHPnj2orKwM6FinT5/G+eefj6qqKrfHpFVWTz/9NO68805UVlZi7dq1KC4uxptvvombb765G++CiCIFgx0iikj3338/XnzxRTQ0NGDixIkYOHCgz+fEx8fDbrertv34xz9GU1MT4uLikJmZ6fW5F110ES666CI88sgjmDZtGl599VXcfPPNHo9JRNGFw1hEFJHuvPNO/Pe//8Wf//znTicmK2VmZuLAgQPYuXMnvv/+e7S1tWHixIkYO3YspkyZgg8//BB1dXX47LPPMH/+fHz55ZewWq2YPXs2qqqqUF9fjy1btmDbtm0YNmyYfMzTp09jw4YN+P777/HDDz/05Nsmoh7AYIeIIpLBYMDUqVPRp08fTJkyxa/nTJ06Fddddx2uvvpq9O/fHytWrIAgCHj//fdx1VVX4Z577sFFF12En/70p6ivr8eAAQOg1Wpx7Ngx3HXXXbjoootw++23Y/LkySgpKQEAXHHFFfjlL3+JO+64A/3798fzzz/fg++aiHqCIIqiGO5GEBF5MmHCBIwYMQJLly4Nd1OIKIox2CGiiHPixAlUVVXh1ltvxd69ezF06NBwN4mIohgnKBNRxBk5ciROnDiBX//616pAZ8SIEaivr/f4nD/+8Y+YPn16qJpIRFGEPTtEFDXq6+ths9k8PjZgwAD07ds3xC0iomjAYIeIiIhiGldjERERUUxjsENEREQxjcEOERERxTQGO0RERBTTGOwQERFRTGOwQ0RERDGNwQ4RERHFNAY7REREFNP+PzDbdTute5UHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data\n",
    "plt.scatter(y_test_1D, RF_predict_1D, marker='o', s = 1, label='Data Points')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_test_predicted')\n",
    "plt.title('RF_n_estimators=120')\n",
    "\n",
    "# # Adding a legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Step 3_RF_model_conc_normalized_n_estimators=120.joblib']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "joblib.dump(classifier_RF, 'Step 3_RF_model_conc_normalized_n_estimators=120.joblib', compress=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_predicted_grouping(predictions):\n",
    "    threshold = 1.0\n",
    "    index = np.argsort(predictions) + 1\n",
    "    prob = 0\n",
    "    grouping = []\n",
    "    probabilities = []\n",
    "    for j in index[::-1]:\n",
    "        prob += predictions[j - 1]\n",
    "        grouping.append(j)\n",
    "        probabilities.append(predictions[j - 1])\n",
    "        if prob >= threshold:\n",
    "            break\n",
    "    # print(grouping)\n",
    "    # print(probabilities)\n",
    "    return grouping, probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "y_predicted_proba = classifier_RF.predict_proba(X_test)\n",
    "print(y_predicted_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage: 99.66177079023582\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "total_iterations = len(y_predicted_proba)\n",
    "for idx_proba, i in enumerate(y_predicted_proba):\n",
    "    # print(idx_proba)\n",
    "    grouping, probabilities = generate_predicted_grouping(i)\n",
    "    if y_test_1D[idx_proba] in grouping:\n",
    "        cnt += 1\n",
    "\n",
    "percentage = (cnt / total_iterations) * 100\n",
    "print(\"Percentage:\", percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(\"Step 3_RF_model_conc_normalized_n_estimators=120.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_predict_1D = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the percentage of correct predictions for each unique pair of true and predicted values\n",
    "count_dict = {} \n",
    "for true_val, pred_val in zip(y_test_1D, RF_predict_1D):\n",
    "    if (true_val, pred_val) not in count_dict:\n",
    "        count_dict[(true_val, pred_val)] = 1\n",
    "    else:\n",
    "        count_dict[(true_val, pred_val)] += 1\n",
    "# print(count_dict)\n",
    "\n",
    "# Calculate total counts for each value starting from 1 to 456 for key[0]\n",
    "total_counts = {}\n",
    "for i in range(1, 457):\n",
    "    total_counts[i] = sum(value for key, value in count_dict.items() if key[0] == i)\n",
    "# print(total_counts)\n",
    "\n",
    "\n",
    "# Calculate percentages\n",
    "percentages = {}\n",
    "for key, value in count_dict.items():\n",
    "    # print(key)\n",
    "    # print(value)\n",
    "    percentages[key] = (value / total_counts[key[0]]) * 100\n",
    "# print(percentages)\n",
    "\n",
    "# Extract x and y data for the scatter plot\n",
    "x_data = [key[0] for key in percentages]\n",
    "y_data = [key[1] for key in percentages]\n",
    "colors = [value for value in percentages.values()]\n",
    "\n",
    "plt.figure(figsize=(11, 8))  # Adjust width and height as needed\n",
    "\n",
    "# Create the scatter plot\n",
    "# plt.scatter(x_data, y_data, c=colors, cmap='viridis', s = 1)\n",
    "plt.scatter(x_data, y_data, c=colors, cmap='Reds', s = 3) #, zorder=1)\n",
    "\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('Label of True Mechanism', fontsize = 24, labelpad=15)\n",
    "plt.ylabel('Label of Predicted Mechanism', fontsize = 24, labelpad=15)\n",
    "# plt.title('Scatter Plot')\n",
    "\n",
    "# Add color bar\n",
    "cbar = plt.colorbar(ticks=[0, 20, 40, 60, 80, 100])\n",
    "cbar.set_label('Percentage', fontsize=18)  # Adjust the fontsize and labelpad as needed\n",
    "# Set font size of ticks in color bar\n",
    "cbar.ax.tick_params(labelsize=16)  # Adjust the font size as needed\n",
    "\n",
    "# Add axis ticks\n",
    "plt.xticks([1, 100, 200, 300, 400, 457], fontsize = 20)  # Adjust the range and step size for x-axis ticks\n",
    "plt.yticks([1, 100, 200, 300, 400, 457], fontsize = 20)  # Adjust the range and step size for x-axis ticks\n",
    "plt.tick_params(axis='both', direction='in')  # Set ticks to be inside the plot\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  }
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